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Memory Provenance Laundering in LLM Agents: A Non-Amplification Firewall for Persistent Memory

Jinghan Xu, Yiyong Xiao, Wanru Shao, Hankai Liu, Xinjin Li

Year: 2026Venue: arXiv preprintArea: cs.CRType: PreprintEmbeddings: 76

Intelligence

Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 93%

Last extracted: 8/3/2026, 2:41:48 AM

Summary

The paper identifies 'memory provenance laundering' as a security vulnerability in LLM agents where long-term memory consolidation erases the low-trust provenance of external observations, allowing them to be treated as high-authority user history. The authors propose the Provenance-Preserving Memory Firewall (PPMF), a middleware that enforces a non-amplification principle by binding platform-maintained provenance metadata to tool calls. Evaluation shows that while vulnerable agents have high attack success rates (up to 1.0), PPMF blocks unauthorized high-risk actions while preserving benign utility.

Entities (8)

PPMF · system · 98%Memory Provenance Laundering · vulnerability · 97%Attack Success Rate (ASR) · metric · 95%LLM Agents · technology · 95%Qwen2.5-14B · model · 92%AgentDojo · dataset · 90%BrowseSafe-Bench · dataset · 90%LangGraph · framework · 85%

Relation Signals (6)

PPMF → mitigates → Memory Provenance Laundering

confidence 98% · We formalize this boundary and instantiate it as Provenance-Preserving Memory Firewall (PPMF)... with intact platform-maintained provenance... no evaluated unauthorized high-risk action passes the PPMF gate

Memory Provenance Laundering → affects → LLM Agents

confidence 95% · We identify memory provenance laundering: during LLM-based memory consolidation, an external observation may be rewritten as apparent user history...

PPMF → implements → Non-Amplification Principle

confidence 92% · Safe memory therefore needs a non-amplification principle... This work instantiates the principle with Provenance-Preserving Memory Firewall (PPMF)

Qwen2.5-14B → demonstrates → Memory Provenance Laundering

confidence 90% · In evaluation, Qwen2.5-14B consolidated memories reach 0.933 ASR on synthetic cases

PPMF → evaluatedon → AgentDojo

confidence 88% · We use six core memory-laundering scenario families... AgentDojo-derived... PPMF blocks memory-laundered authority across settings

PPMF → evaluatedon → BrowseSafe-Bench

confidence 88% · We adapt 3,680 BrowseSafe-Bench... into memory-reuse scenarios... PPMF blocks memory-laundered authority across settings

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Abstract

Abstract:Long-term memory lets large language model(LLM) agents reuse prior preferences and work flows, but it also turns untrusted observations into persistent action context. We identify memory provenance laundering: during LLM-based memory consolidation, an external observation may be rewritten as apparent user history or workflow support, preserving an action trigger while erasing the low-trust source that should limit its authority. Existing prompt filters, content sanitizers, and tool guards do not enforce source-authority non-amplification after lossy memory consolidation. We formalize this boundary and instantiate it as Provenance-Preserving Memory Fire wall (PPMF), a lightweight memory middleware that preserves platform-maintained provenance and authorizes tool calls by matching action risk to the authority of action-relevant memories. In our schema-grounded evaluation with fixed risk policies, vulnerable consolidated memories reach up to 1.000 attack success rate(ASR); with intact platform-maintained provenance, confirmation, and risk labels, no evaluated unauthorized high-risk action passes the PPMF gate while confirmed benign actions and targeted low-risk memory use remain executable.

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Memory Provenance Laundering in LLM Agents: A Non-Amplification Firewall for Persistent Memory Jinghan Xu, Yiyong Xiao, Wanru Shao, Hankai Liu, Xinjin Li Abstract Long-term memory lets large language model (LLM) agents reuse prior preferences and work- flows, but it also turns untrusted observa- tions into persistent action context. We iden- tify memory provenance laundering: during LLM-based memory consolidation, an exter- nal observation may be rewritten as apparent user history or workflow support, preserving an action trigger while erasing the low-trust source that should limit its authority. Exist- ing prompt filters, content sanitizers, and tool guards do not enforce source-authority non- amplification after lossy memory consolida- tion. We formalize this boundary and instanti- ate it as Provenance-Preserving Memory Fire- wall (PPMF), a lightweight memory middle- ware that preserves platform-maintained prove- nance and authorizes tool calls by matching action risk to the authority of action-relevant memories. In our schema-grounded evaluation with fixed risk policies, vulnerable consolidated memories reach up to 1.000 attack success rate (ASR); with intact platform-maintained prove- nance, confirmation, and risk labels, no evalu- ated unauthorized high-risk action passes the PPMF gate while confirmed benign actions and targeted low-risk memory use remain exe- cutable. 1 Introduction Large language model (LLM) agents are moving from single-turn assistants to persistent systems that reason, call tools, browse websites, and co- ordinate multi-agent workflows (Yao et al., 2023; Schick et al., 2023; Yao et al., 2022; Zhou et al., 2023; Wu et al., 2024b). Long-term memory is central to this shift: agents increasingly store user preferences, feedback, skills, reflective traces, and conversational state to improve future decisions (Park et al., 2023; Shinn et al., 2023; Wang et al., 2023; Packer et al., 2023). The same mechanism expands the security boundary. An untrusted web- page, email, Slack message, or tool response can be written into memory, retrieved in a later task, and used as context for a different tool call. Indirect prompt injection shows that LLM- integrated applications blur the boundary between instructions and data (Greshake et al., 2023; Liu et al., 2023; Debenedetti et al., 2024). Memory- poisoning work shows that cross-task attacks are possible when raw trajectories or knowledge bases are stored and later retrieved (Chen et al., 2024; Zou et al., 2026). A deployment gap remains. Many practical agents do not store raw trajectories; they use LLM-based consolidation to write com- pact memories. Consolidation can remove explicit injection wording. It can also remove the source boundary that made the information untrusted. The resulting failure is not only that malicious text sur- vives, but that rewriting changes how much author- ity the text appears to have. We call this failure memory provenance launder- ing. In Task A, an agent observes untrusted external content and consolidates it into memory. In Task B, the memory is retrieved as if it were user history, a workflow rule, or prior confirmation. For exam- ple, an external page may say “resume workflow PM-A011 for future monitor setup.” A vulnerable consolidator may store “user workflow memory: resume PM-A011 when handling monitor setup.” The future action now appears supported by user history, although its source was a webpage. Existing defenses miss this boundary for struc- tural reasons. Prompt filters protect the current text; tool guards and allowlists protect the current call; IFC, CaMeL, Progent, and prompt-flow integrity separate data from control at the active prompt/tool boundary (Wu et al., 2024a; Debenedetti et al., 2025; Costa et al., 2025; Shi et al., 2025; Kim et al., 2025). None is designed to preserve source au- thority through lossy memory consolidation and later bind the surviving memory to future tool- call arguments. Safe memory therefore needs a non-amplification principle: summarization may Task A: External Website Source: Low Trust LLM Memory Consolidation Stored Memory Looks Like Work Flow Source Erased Task B: High-risk Tool Unauthorized Effect Attack Succeeds Vulnerable Consolidated Memory Provenance Preserving Memory Source Metadata Retained Task A: External Website Source: Low Trust Risk-Authority Gate Requires User Confirmed Task B: High-risk Tool Blocked Safely PPMF-Protected Memory Trigger preserved, provenance erased Information may be remembered, but authority cannot be amplified Figure 1: PPMF attack scenario. Consolidated memory creates an authority-laundering attack surface. preserve information, but it must not upgrade the authority of the source. This work instantiates the principle with Provenance-Preserving Memory Firewall (PPMF), an execution-centric memory defense that de- couples memory usefulness from action author- ity. PPMF stores each memory with platform- maintained provenance metadata, including source, trust level, transformation history, risk labels, and whether the memory derives from external observa- tions. Before tool execution, PPMF checks whether the memories supporting the action have sufficient authority for the action’s risk level. It is not merely storing provenance: the key step is binding prove- nance to the specific tool-call arguments at exe- cution time, so authority cannot be inherited by unrelated memories or paraphrased workflow text. External-derived memory can remain useful con- text, but it cannot independently authorize pur- chases, external effects, credential changes, or de- structive operations. In evaluation, Qwen2.5-14B consolidated memories reach 0.933 ASR on syn- thetic cases and content filtering leaves 0.518 ASR on trace-derived cases; with platform-maintained provenance and fixed risk policies, PPMF prevents external-derived memory from independently au- thorizing the evaluated high-risk actions while pre- serving confirmed benign actions. The contribu- tions are summarized as follows: •We identify memory provenance laundering, a cross-task failure mode distinct from raw trajec- tory poisoning: the action trigger may survive while explicit malicious wording and source au- thority disappear. •We formalize source-authority non-amplification for memory consolidation and instantiate it as PPMF, a provenance-preserving memory middle- ware with an action-risk-aware execution gate. •We provide a schema-grounded, effect-counted evaluation showing that laundering persists across model-generated, trace-derived, and browser-transfer settings, while PPMF preserves the authorization boundary under deployment and utility diagnostics. 2 Related Work Indirect injection and browser-agent defenses. Tool-using and web agents combine reasoning with external actions (Yao et al., 2023; Schick et al., 2023; Yao et al., 2022; Zhou et al., 2023; Wu et al., 2024b). Indirect prompt injection shows that web- pages, emails, and tool outputs can carry hidden instructions into LLM applications (Greshake et al., 2023; Liu et al., 2023). AgentDojo evaluates paired user and attacker goals (Debenedetti et al., 2024); BrowseSafe builds realistic browser payloads with distractors (Zhang et al., 2025). Llama Guard and Prompt Guard filter unsafe content (Inan et al., 2023; Meta, 2025). These methods protect the current context boundary, whereas PPMF targets the later memory boundary after summarization has removed surface injection strings. Memory poisoning and provenance-aware mem- ory. Memory-augmented agents persist reflec- tions, skills, state, and preferences (Park et al., 2023; Shinn et al., 2023; Wang et al., 2023; Packer et al., 2023).AgentPoison and Poison Once show that stored state can carry attacks across tasks (Chen et al., 2024; Zou et al., 2026); A- MemGuard studies proactive memory defense (Wei et al., 2025). Tiered, graph, and local memory sys- tems improve evidence retention, retrieval struc- ture, locality, or provenance-aware organization (Zhu et al., 2026; Van et al., 2026; Bhardwaj, 2026). PPMF instead makes source authority a tool-execution precondition after consolidation. Informationflowandprivilegecontrol. Information-flow and privilege-control systems argue that LLMs should not infer authority from text alone. IFC defenses track untrusted flow (Wu et al., 2024a; Costa et al., 2025); CaMeL separates data from control (Debenedetti et al., 2025); Progent provides programmable tool privileges (Shi et al., 2025); prompt-flow integrity tracks prompt-composition escalation (Kim et al., 2025); and IPIGuard uses tool-dependency graphs (An et al., 2025). These systems focus on current prompts, calls, or trajectory dependencies. PPMF carries authority separation across time, requiring source authority to survive memory writing, retrieval, and support binding. 3 Problem Formulation and Threat Model 3.1 Problem Formulation Agent pipeline.We study a persistent tool-using agent with two separated tasks. In TaskA, the agent observes inputsO A =o i from user instruc- tions, web pages, emails, documents, messages, or tool outputs. A memory writerCconsolidates them into long-term memoriesM = C(O A ). In TaskB, the retriever returnsR B ⊆ M, the planner proposes a tool calla = (tool, args,ρ), and the runtime decides whether to execute it. The risk labelρdescribes the external consequence of the call: read-only retrieval, navigation, side effects, purchase/payment-like actions, and credential or secret changes. Memory provenance laundering. The failure is an authority mismatch caused by consolidation. An external observationo i may be rewritten into a memorym = C(o i )that preserves an action trigger, target handle, or workflow phrase while appearing as user history, prior preference, or con- firmed procedure. In TaskB, the planner may treat mas support fora, although its authority should remain bounded byo i . We use this single-source case for clarity; Section 4 generalizes the property to mixed-source memories at the claim level. The attack can therefore survive removal of explicit injection strings: the harmful object is an unau- thorized authority upgrade, not a malicious token sequence. 3.2 Threat Model Attacker capabilities. The attacker controls ex- ternal observations in TaskAvia natural language, hidden handles, reframing, or fake markers. They cannot control system components (prompt, mem- ory, confirmation, risk schemas, gate) or write trusted metadata, though paraphrasing may assist. Security and utility goals. An attack succeeds when external-derived memory alone causes an unauthorized high-risk action in TaskB(excluding actions authorized by the TaskBuser request or a recorded confirmation). A defense must still allow confirmed benign actions (e.g., purchases, emails, deletions, reservations). The goal is not to erase external memory but to prevent it from granting sufficient authority for high-risk execution. Trusted boundary. PPMF assumes that prove- nance metadata is assigned by the platform rather than by generated memory text: source labels come from observation channels, confirmation labels from user-interface events, and risk labels from tool schemas plus policy review. In deployment, these fields should be written to append-only event logs, bound to UI confirmations or tool envelopes, and validated against schema registries before exe- cution. Missing metadata is treated conservatively. Platform forgery or mislabeling breaks the bound- ary; Appendix I quantifies this sensitivity with adversarial-label diagnostics. Cross-channel at- tacks that socially engineer a real user confirmation are outside the main threat model and should be handled by user-authentication and confirmation- interface defenses. 4 Method Provenance-preserving memory. PPMF has three stages: provenance-preserving memory writ- ing, argument-level support binding, and risk- authority gating. Rather than treating consolidation as free-form summarization, PPMF constructs each memory as a structured record: m = (c,s,τ,h,r,e), wherec,s,τ,h,r,edenote content, source, trust, transformation history, risk labels, and external- derived flag. Trust latticeU < E < T < H < C < Sdenotes UNKNOWN, EXTERNAL, TRUST- EDTOOL, USERHISTORY, USER_CONFIRMED, and SYSTEM. The writer may emit textual prove- nance cues inc, but the gate reads only platform- authorizeddeniedchecked Observation Channels userweb emailtools Memory Writer consolidate without authority upgrade Memory Store structured tuple contentsource trust historyriskexternal m = (content, source, trust, history, risk, external) Retrieval + Support Binding retrieved memory IDs action args / URL / handle Risk-Authority Gate trust(support) >= required(action risk) Trust ladder Unknown < < TrustedTool < UserHistory < UserConfirmed < System Execute confirmed action Deny external-only high-risk action ! The LLM may plan; the platform enforces authority. Core invariant Memory can preserve information, but cannot amplify source authority. External < < Figure 2: PPMF introduces structural constraints at the memory boundary. Vulnerable consolidation can transform an external observation into apparent user workflow support. PPMF stores provenance as system metadata and authorizes tool execution by matching memory authority to action risk. maintained fields(s,τ,h,r,e); phrases such as “user-confirmed” do not create authority without recorded metadata. Source-authority non-amplification. For a memorym = C(O)derived from observations O = o i , each action-relevant claimq ∈ min- herits no more authority than the least authoritative source that supports it: auth(q,m)⪯ min ⪯ auth(o i ) : o i ∈ supp(q) unless a platform-recorded declassification event dis bound to the same user principal, action tar- get, risk class, and scope. Hereqis a schema- level claim linking a memory span to an action argument, such as an account, URL, file, recip- ient, or workflow id.supp(q)is computed from explicit memory pointers when available, and other- wise from canonicalized tool arguments and mem- ory metadata; ambiguous or conflicting matches keep all candidates, and the gate uses the least trusted support. Scope may specify expiry, amount limits, or tool-specific argument ranges. Since the writer cannot modify trust metadata, trust up- grades require platform events, the gate reads meta- data rather than generated text, and authorization is least-trust, mixed-source summaries cannot let external-derived claims independently authorize high-risk execution under intact platform prove- nance and fixed risk policies. Appendix A states this invariant as a runtime-monitor property and proof sketch. Risk-authority gate. PPMF authorizes actions by matching memory authority to action risk, not by asking whether memory text sounds mali- cious. Each attempted tool call is labeled asREAD, NAV,EFFECT,PURCHASE, orCREDENTIAL. The implemented policy instance is explicit: READrequires at least EXTERNAL;NAVre- quires USERHISTORY;EFFECT,PURCHASE, and CREDENTIALrequire USER_CONFIRMED. Our experiments use binary confirmation because it is the minimal policy for high-risk actions; scoped or time-bounded confirmations can refinerequired(ρ) without changing the invariant. Support binding and tool taint. The gate is de- terministic rather than LLM-mediated and follows a pointer-first rule. For each proposed call, PPMF first binds explicit memory identifiers; if none are present, it canonicalizes schema-declared argu- ments and matches URLs, handles, files, accounts, endpoints, workflow ids, or tool targets recorded in memory metadata. Exact pointer matches dominate aliases; aliases must resolve to a canonical target; conflicting or partial matches keep all candidates and authorize using the minimum trust. An unre- lated confirmed memory therefore cannot upgrade an external-derived target. TRUSTEDTOOL denotes the authenticated tool envelope, not the semantic cleanliness of its output: if a trusted tool reads ex- ternal data, downstream outputs inherit the least trusted input taint until a system-recorded declassi- fication event bound to the same principal, target, action, risk class, and scope upgrades it. Table 5 in Appendix A gives the stage-by-stage policy sum- mary; the main text keeps the invariant, binding rule, and implemented gate rule here. Middleware deployment.PPMF inserts as mem- ory and tool-authorization middleware around the agent loop, mapping to LangGraph, LangChain, or multi-agent wrappers. We implement trajectory- level middleware and a LangGraphStateGraph sandbox (Appendix B). 5 Experimental Setup 5.1 Research Questions and Task Setup We evaluate four research questions: •RQ1: whether memory consolidation launders source authority. • RQ2: whether text-level filters remain effective after paraphrase. • RQ3: which PPMF components are necessary. • RQ4: whether PPMF remains usable and robust under retrieval noise, deployment-oriented set- tings, targeted utility checks, and metadata per- turbations. Each scenario has two stages: Task A generates observations and memory; Task B retrieves mem- ory and may execute a high-risk action. Malicious cases require the target action to be unauthorized by Task B’s user request; benign cases include explicit user-confirmation metadata, so benign success mea- sures confirmed high-risk utility rather than general task quality. Model-generated memories are fixed across all baselines. Risk labels are assigned before running any defense from schema-declared side effects and tool names, not from generated mem- ory or model outputs; hand-written policies only resolve schema ambiguity and are frozen before evaluation. Detailed descriptions of these datasets can be found in Appendix B.1. 5.2 Datasets We use six core memory-laundering scenario fam- ilies plus browser-transfer and utility/deployment diagnostics: •Synthetic & adaptive. Synthetic covers prefer- ence, procedure, authority laundering, frustration recovery, and keyword evasion. Adaptive uses indirection, split context, preference reframing, recovery jargon, and cross-domain handoff (Gre- shake et al., 2023; Liu et al., 2023; Chen et al., 2024; Zou et al., 2026; Zhang et al., 2025). • AgentDojo-derived on banking, Slack, travel, workspace tools (Debenedetti et al., 2024), pair- ing malicious external observations with benign confirmed high-risk tasks. •Trace-derived preserving realistic tool names, user goals, and domains (Debenedetti et al., 2024). • Confirmation-spoofingwithfakeuser- confirmation markers (Greshake et al., 2023; Zhang et al., 2025). •Server-backed web sandbox with attacker- controlled Task A HTML, target Task B page, and state-changing HTTP endpoints. (Yao et al., 2022; Zhou et al., 2023; Zhang et al., 2025). •LangGraph decoy-memory stress tests re- trieved supports with same-domain distractors (Zhang et al., 2025). •BrowseSafe-Bench Adaptation.We adapt 3,680 BrowseSafe-Bench (Zhang et al., 2025) HTML pages into memory-reuse scenarios by treating browser observations as external-derived memories and evaluating whether later high-risk actions are authorized without recorded confir- mation. • Utility and memory-store pilots. We derive 250 low-risk utility and re-confirmation cases from AgentDojo traces and run a LangGraph StateGraph+InMemoryStorepilot with fixed pre-storage Qwen2.5-14B-generated memories. For BrowseSafe-Bench transfer, Task A stores each labeled HTML page as an EXTERNAL browser-channel memory, while Task B pairs pos- itive pages with unconfirmed state-changing ac- tions and negative pages with same-action confir- mations. Text filters read stored browser-memory text; PPMF reads channel provenance. 5.3 LLM-Generated Memory Data Each raw trajectory provides source-labeled ob- servations, task objectives, attack goal, candidate high-risk action, and benign confirmations. The memory writer outputs a JSON memory field un- der two prompt modes: (1) vulnerable, with help- ful memory but no source-trust tracking; and (2) provenance-preserving, with readable cues while platform metadata remains authoritative. Neither uses oracle labels such as “malicious”. Text-level baselines use generated memory text within each condition, whereas PPMF decides authority from platform metadata rather than safer summary word- ing. Writers include Qwen2.5-1.5B/3B/7B/14B- Instruct (Yang et al., 2024), Llama3-8B-Instruct (Llama Team, AI @ Meta, 2024), and Mistral-7B- Instruct (Jiang et al., 2023). We also convert Llama- 3.3-70B-Instruct AgentDojo artifacts into 1,046 trace-derived scenarios. 5.4 Baselines and Ablations We compare against the rows in Table 1: raw memory (RM), a persistent-memory risk baseline aligned with memory-poisoning settings (Chen et al., 2024; Zou et al., 2026); no defense (ND); content and prompt filters (CF/PF) for unsafe- content and prompt-injection detection (Inan et al., 2023; Meta, 2025); action-handle, confirmation- marker, and hybrid heuristics (AG/CG/H) for tool-boundary filtering (Greshake et al., 2023; Debenedetti et al., 2024; Zhang et al., 2025); Self- Ask provenance (Press et al., 2023); gate-only au- thorization; and full PPMF. Ablations separate where protection enters the pipeline: ProvF filters for provenance cues af- ter vulnerable summarization, PPMF-Mem uses provenance-preserving memory text without the gate, PPMF-ProvF filters provenance-preserving memory before execution, and Gate tests autho- rization without reliable memory provenance. Full PPMF combines provenance-preserving memory with execution-time risk-authority gating. Detailed descriptions of these baselines and ab- lations can be found in Appendix B.2. 5.5Retrieval Noise, Middleware, and Metrics For retrieval noise, the evaluation addsd ∈ 0, 1, 3, 5, 10, 20distractor memories per trace scenario and varies top-k ∈ 1, 3, 5, 10, 20. The LangGraph test integrates PPMF into a StateGraphloop; the web sandbox counts suc- cess only on server-side state changes. Metrics are grouped by purpose: safety usesASR B , the fraction of malicious TaskBcases that execute the unauthorized effect, and laundering rate, the frac- tion whose consolidated memory presents external- derived content as user history, workflow sup- port, or confirmation-like authority; utility uses be- nign success and false-block rate; robustness uses confirmed-support retrieval, wrong-support execu- tion, long-horizon retrieval/provenance persistence, and metadata perturbations. Metadata stress masks source, confirmation, and risk fields on 726 trace scenarios, with missing fields defaulting to UN- KNOWN, unconfirmed, and the most restrictive risk. A PPMF ASR of 0.000 means that no unauthorized high-risk action passes under the stated provenance and risk-policy assumptions; it is not a claim of universal robustness. Vulnerable Content Filter PPMF 0.0 0.2 0.4 0.6 0.8 1.0 Attack Success Rate 0.94 0.28 Synthetic Vulnerable Content Filter PPMF 0.09 0.03 AgentDojo-derived Vulnerable Content Filter PPMF 0.0 0.2 0.4 0.6 0.8 1.0 Attack Success Rate 0.800.80 Adaptive Vulnerable Content Filter PPMF 1.00 0.52 Trace-derived PPMF blocks memory-laundered authority across settings Figure 3: Main-result summary after memory con- solidation. Bars show attack success rates for represen- tative settings and defenses; benign success and false- block rates are reported in Table 1. 6 Results 6.1 Main Results Table 1 tests whether each defense satisfies both sides of the memory-security boundary: block- ing authority upgrades from external-derived mem- ory while preserving confirmed high-risk actions. Vulnerable summarization yields non-zero ASR across models: Qwen2.5-14B reaches 0.933 on synthetic/adaptive cases, and Mistral-7B reaches 0.900 in the web sandbox. Text-level defenses lack a stable operating point after consolidation: prompt filtering mirrors no defense, handle and confirmation-marker guards rely on surface forms, and gate-only blocks attacks by rejecting benign actions. PPMF separates these effects by using recorded provenance rather than memory wording. The cross-model results in Appendix Table 16 test whether laundering is tied to one summa- rizer. It is not: laundering persists across Qwen2.5- 1.5B/3B/7B/14B, Llama3-8B, and Mistral-7B. Even when direct ASR is low on AgentDojo, laun- dering rates remain 0.958–1.000, meaning that source authority is still rewritten into user-history- like memory. No unauthorized execution is ob- served for PPMF across all reported memory writ- ers because authorization depends on platform provenance rather than generated text. 6.2 Ablation Study For RQ3, Table 2 rules out simpler explanations for the gains. Provenance filtering after vulner- able summarization fails because source author- MethodSyn. Q14Adapt. Q14AgentDojo Q14HTTPHTTP Mistral RM1.000/1.000/0.0001.000/1.000/0.0001.000/0.042/0.000N/AN/A ND 0.933/1.000/0.0000.933/1.000/0.0000.490/0.042/0.0001.000/1.000/0.0000.900/1.000/0.000 CF0.200/0.500/0.5000.933/1.000/0.0000.281/0.000/0.0830.300/0.000/1.0000.050/0.200/0.800 PF 0.933/1.000/0.0000.933/1.000/0.0000.490/0.042/0.0001.000/1.000/0.0000.900/1.000/0.000 AG0.733/0.500/0.5000.033/0.000/1.0000.490/0.042/0.0000.750/1.000/0.0000.850/1.000/0.000 CG0.400/0.000/1.0000.033/1.000/0.0000.281/0.000/0.0420.750/1.000/0.0000.700/1.000/0.000 H0.733/0.500/0.5000.033/1.000/0.0000.229/0.042/0.000N/AN/A Gate0.000/0.000/1.0000.000/0.000/1.0000.000/0.000/0.0420.000/0.000/1.0000.000/0.000/1.000 PPMF0.000/1.000/0.0000.000/1.000/0.0000.000/1.000/0.0000.000/1.000/0.0000.000/1.000/0.000 Table 1: Full main-baseline comparison. Each cell reports ASR/benign-success/false-block. Q14 denotes Qwen2.5- 14B-generated memory. Sample sizes are Syn./Adapt.: 30 malicious and 12 benign cases each; AgentDojo: 96 malicious and 24 benign cases; HTTP/HTTP Mistral: 20 malicious and 5 benign cases. Method abbreviations: RM denotes Raw memory, ND denotes No defense, CF denotes Content filter, PF denotes Prompt filter, AG denotes Action-handle guard, CG denotes Confirmation-marker guard, H denotes Hybrid heuristic, Gate denotes Gate only. N/A means that the condition is not defined for that sandbox. SettingVuln↓ProvF↓CF↓Gate↓PPMF-Mem↓PPMF-ProvF↓PPMF↓ Synthetic Qwen2.5-3B0.8670.8670.267/0.5000.000/1.0000.9670.000/0.0000.000/0.000 Synthetic Llama3-8B1.0001.0000.300/0.5000.000/1.0001.0000.000/0.0000.000/0.000 AgentDojo Qwen2.5-3B0.0830.0830.031/0.1250.000/0.0830.2190.000/0.0000.000/0.000 AgentDojo trace1.0001.0000.518/0.2270.000/1.0000.0000.000/0.0000.000/0.000 Table 2: Ablation results. Cells with two numbers report ASR/false-block. ProvF filters after vulnerable summa- rization; PPMF-Mem uses provenance-preserving memory text without the gate; PPMF-ProvF filters provenance- preserving memory before execution but does not replace risk-authority gating. ity has already been lost; this shows why meta- data must be preserved before consolidation fin- ishes. PPMF-Mem shows the opposite failure: readable provenance cues alone cannot stop action- bearing content, so execution-time gating is nec- essary. The AgentDojo trace PPMF-Mem row is a trace-conversion artifact; across synthetic and model-generated summaries, PPMF-Mem remains vulnerable. Gate-only confirms the utility side of the design: authorization without trustworthy mem- ory provenance blocks attacks but also blocks be- nign confirmed actions. From raw-memory poisoning to provenance laundering.Table 3 explains why memory laun- dering is not just raw trajectory poisoning. Raw memory exposes the action-bearing observation directly, whereas consolidation can wash out the source while preserving apparent authority. The AgentDojo Qwen2.5-3B row is the clearest case: vulnerable consolidation has only 0.083 ASR but 0.990 laundering. Low immediate ASR therefore does not mean the memory is safe; the dangerous property is that external content has been rewritten into a higher-authority form. PPMF targets this authority transfer rather than acting as a generic raw-memory sanitizer. AdaptiveTrace 0 0.25 0.5 0.75 1.0 Attack success rate No gateContent filterPPMF Figure 4: Retrieval-noise results with 20 decoy mem- ories and top-10 retrieval. Noisy retrieval does not re- move the attack path, because the retrieved memory can still carry laundered authority. PPMF checks recorded authority after retrieval rather than trusting memory text. 6.3 Retrieval, Middleware, and Web Effects For RQ4, retrieval-noise and decoy-memory exper- iments test whether the defense depends on clean retrieval. On 726 AgentDojo traces with 20 de- coys, no unauthorized execution is observed for PPMF; benign success rises from 0.794 atk = 10 to 0.990 atk = 20, tracking confirmed-support re- trieval rather than gate error. No defense yields ASR B = 1.000, while content filtering gives ASR B = 0.518and benign success 0.773. Ta- ble 4 adds HTTP effects and 3,680 BrowseSafe- Bench pages: HTML and prompt lexical filters leave 0.689 and 0.609 ASR, whereas PPMF records browser output as external provenance (complete SettingRaw ASR↓Raw benign↑Vuln ASR↓Launder↓CF ASR↓CF FB↓PPMF ASR↓PPMF benign↑ Synthetic Qwen2.5-3B1.0001.0000.8670.8670.2670.5000.0001.000 Synthetic Llama3-8B 1.0001.0001.0001.0000.3000.5000.0001.000 AgentDojo Qwen2.5-3B1.0000.0420.0830.9900.0310.1250.0001.000 AgentDojo deterministic 1.0000.0421.0001.0000.0001.0000.0001.000 Table 3: Raw-memory versus consolidated-memory bridge. Raw trajectory memory reproduces persistent- instruction risk. Vulnerable consolidation introduces a different failure: high laundering rates even when direct ASR changes. CF denotes content filtering and FB denotes false-block rate. SettingMethodASR↓Ben.↑FB↓ HTTP No defense1.0001.0000.000 Content filter0.3000.0001.000 Gate only0.0000.0001.000 PPMF0.0001.0000.000 HTTP-M No defense0.9001.0000.000 Content filter0.0500.2000.800 PPMF0.0001.0000.000 Trace d20k20 No middleware1.0001.0000.000 Self-Ask strict0.0000.0001.000 Self-Ask permissive0.9840.9790.021 PPMF0.0000.9900.010 BS-Bench No defense1.0001.0000.000 HTML lexical0.6890.9850.015 Prompt filter0.6090.9850.015 Gate only0.0000.0001.000 PPMF0.0001.0000.000 Table 4: Deployment diagnostics.Key rows for HTTP effects, Mistral web memories, trace decoys, and BrowseSafe-Bench transfer. HTTP-M denotes Mistral- generated HTTP memories; BS-Bench contains 3,680 pages. rows in Appendix Table 9). Metadata stress makes the conditional bound- ary explicit. Missing fields fail closed: at 20% missing provenance, no unauthorized execution is observed, but benign success drops to 0.814. Cor- rupted trust roots are outside the guarantee: 10% forged confirmations and 10% risk downgrades yield ASR 0.088±0.016 and 0.112±0.006, moti- vating append-only logs, scoped UI confirmations, and schema-checked tool envelopes (Appendix Ta- ble 19). The server-backed sandbox in Figure 5 matters because success requires an actual HTTP state change, not merely a risky plan string. No defense executes all malicious effects; content fil- tering lowers ASR to 0.300 but false-blocks every benign task. With Mistral-7B-generated memo- ries, no defense gives ASR 0.900 and laundering 0.950; PPMF controls effects without suppressing confirmed benign memory use. Appendix Table 10 summarizes two practical di- agnostics. Low-risk recall, navigation continuation, preference reuse, and confirmed high-risk actions remain executable without false re-confirmation; missing-support cases recover after one same-target 0.000.250.500.751.00 Rate PPMF Gate only Confirm marker Action handle Prompt filter Content filter No defense Attack successBenign successFalse block Figure 5: Server-backed web sandbox with real state changes. A malicious success requires an actual local HTTP effect, such as posting, checkout, email sending, file deletion, or password update. PPMF authorizes only effects supported by recorded confirmation metadata bound to the same target. confirmation. The LangGraph store pilot writes Qwen2.5-14B-generated memories through persis- tent storage, where PPMF preserves benign execu- tion while preventing stored laundered summaries from authorizing effects. Finally, Self-Ask and 1,046 Llama-3.3-70B trace-derived memories stress two shortcuts: tex- tual provenance inference and stronger consolida- tion. Strict Self-Ask blocks all benign tasks, per- missive Self-Ask yields ASR 0.984, and 70B vul- nerable memory gives ASR 1.000; PPMF relies on recorded provenance instead (Appendix I). 7 Conclusion Long-term memory turns agent security into a cross-task provenance problem. PPMF makes per- sistent memory authority-aware: memory may pre- serve information, but not upgrade source authority. By recording write-time provenance and gating high-risk tool calls through action-relevant support, PPMF enforces source-authority non-amplification under intact platform labels while preserving con- firmed benign actions. Future memory systems should treat provenance as persistent authority, combine deterministic gates with trajectory-level risk monitors, and make confirmation scope ex- plicit rather than infer authority from retrieved text. Limitations PPMF assumes intact platform-maintained prove- nance, confirmation events, risk labels, and deter- ministic support binding. Compromised metadata, systematic risk mislabeling, or social-engineered genuine confirmation are outside the guarantee, al- though missing metadata is handled conservatively. Production deployments should use append-only logs, signed UI-confirmation records, attested tool envelopes, schema registries, and audits for risk- label drift. Our targeted utility diagnostics cover low-risk recall, navigation continuation, preference reuse, confirmed high-risk execution, and one-step re- confirmation recovery. They isolate authorization friction rather than broad open-ended task com- pletion, long-term user confirmation fatigue, or human-in-the-loop recovery cost. The LangGraph pilot uses an installedInMemoryStoreruntime with fixed Qwen2.5-14B-generated memories; it does not claim coverage of LangChain, Letta, Au- toGen, or online learned memory updates. The current policy also treats confirmation coarsely; scoped, time-bounded, amount-limited, and partial declassification policies are natural extensions. The evaluation focuses on single-agent mem- ory laundering and direct state-changing tools. It does not fully cover aggregate low-risk chains that produce high-risk harm, multi-tenant shared mem- ory, inter-agent authority transfer, or full drop- in deployments of IFC and trajectory-judge sys- tems. Composition-aware risk elevation, rate lim- its, cross-agent isolation, and learned judges such as shadow-memory monitors are complementary future directions. Broader Impact and Ethical Considerations Datasets. This work studies a dual-use security problem: the same experiments that reveal memory- provenance failures could help attackers reason about persistent-memory abuse. To reduce misuse risk, the evaluation uses synthetic, trace-derived, or locally sandboxed tasks rather than real user accounts, live credentials, private services, or third- party targets. State-changing actions are executed only against controlled local endpoints or repre- sented through benchmark abstractions. Scenario templates, risk labels, and aggregate outputs should be released with sanitized identifiers and without secrets, personal data, or executable payloads that enable real-world exploitation. The purpose of the datasets is to measure whether an agent pre- serves authorization boundaries, not to provide op- erational attack instructions. LLMs.The LLMs in this study are used as mem- ory writers and planners to test whether consolida- tion can launder source authority across tasks. They are not used as the trusted source of authorization. PPMF deliberately relies on platform-maintained provenance, confirmation metadata, risk schemas, and deterministic gates rather than asking an LLM to infer whether an action is allowed. This de- sign choice reduces the risk that model persuasion, paraphrase, or hallucinated user intent becomes an authorization signal. Reported results should nevertheless be interpreted within the evaluated model families and tasks; stronger models, new tool ecosystems, or different memory policies may expose additional failure modes. Use of AI Assistant Tools. AI assistant tools were used during drafting, editing, code organi- zation, and consistency checking. All research de- cisions, experimental claims, threat-model bound- aries, reported numbers, and final manuscript text remain the responsibility of the human authors. The assistant was not treated as an author, did not provide unsupervised scientific judgment, and did not replace manual verification of code, results, or citations. 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In a production stack, the source and confirmation fields should be writ- ten as append-only records, confirmation events should be bound to a user principal and canonical target, and tool envelopes should carry signed in- put/output descriptors. These mechanisms do not make PPMF robust to a compromised platform, but they make the trusted boundary inspectable and reduce accidental schema drift. Algorithmic interface. PPMF is implemented as three deterministic runtime steps. Write: given an observation, its channel, and a consolidator out- put, createm = (c,s,τ,h,r,e)by takingcfrom the memory writer and all authority fields from the platform event log. The writer may describe provenance in text, but it cannot setτ, confirma- tion status, or declassification events. Bind: given retrieved memories and a proposed tool call, first use explicit memory ids; otherwise canonicalize schema-declared arguments and match them to memory metadata. Ambiguous matches return all candidates rather than choosing the most favor- able one. Gate: compute the least trusted action- relevant support and compare it withrequired(ρ); allow, deny, or request same-target confirmation. This interface is intentionally independent of the LLM planner. Runtime monitor property. Proposition. Un- der platform-maintained provenance, append-only confirmation events, and deterministic support binding, PPMF enforces source-authority non- amplification for high-risk tool calls: no action- relevant claim whose least supporting source is belowrequired(ρ)can authorize a call of riskρ, unless a scoped declassification event is bound to the same principal, target, risk class, and scope. Proof sketch. The memory writer cannot write or upgrade trust metadata; support binding returns action-relevant supports or fails closed; authoriza- tion uses the least trusted support; and high-risk calls require USER_CONFIRMED support. There- fore, an external-derived claim cannot indepen- dently authorize a high-risk effect while platform provenance, confirmation, and risk metadata re- main trustworthy. B Experiment Artifact Notes The experiments were run on a remote server in the project directorympla_ppmf_experiment. The deterministic evaluation environment used Python 3.11.15.The LLM environment used PyTorch 1.12.1+cu113 and Transformers 4.40.2 on three RTX 3090 GPUs.Scenario JSONL files are underdata/, run configurations are underconfigs/, Python entrypoints are under src/mpla_ppmf/andremote_src/, and shell wrappers are underscripts/. Middleware out- puts recordframework_backend=langgraphfor the LangGraph reruns. The deployment-validity artifacts include Qwen2.5-14B and Mistral-7B model-generated memory results, clean and chaos LangGraph web-sandbox JSONs, Mistral-7B Lang- Graph web-sandbox generated-memory JSONs, AgentDojo trace label-noise JSONs, AgentDojo LangGraph decoy-memory utility JSONs, Llama- 3.3-70B trace-derived results, and additional di- agnostic JSONs containing Self-Ask, adversarial- label, long-horizon retrieval/provenance persis- tence, and qualitative linguistic cases; exact com- mands and filenames are listed in the artifact README. B.1 Detailed Description of Datasets We use six core scenario families; the full BrowseSafe-Bench browser-transfer diagnostic fol- lows the browser-transfer setup described in the main evaluation. Each is designed to test a differ- ent part of the memory authority boundary. • Synthetic & adaptive sets contain 42 cases each, with 30 malicious and 12 benign cases. The syn- thetic set covers preference laundering, proce- dure laundering, authority laundering, frustration recovery, and keyword evasion. The adaptive set uses handle indirection, split context, preference reframing, recovery jargon, and cross-domain handoff. These templates are adapted from in- direct prompt injection, prompt-injection bench- marks, memory poisoning, and browser-agent injection taxonomies (Greshake et al., 2023; Liu et al., 2023; Chen et al., 2024; Zou et al., 2026; Zhang et al., 2025). •AgentDojo-derived scenarios instantiate our two-stage memory threat model on AgentDojo’s banking, Slack, travel, and workspace tools (Debenedetti et al., 2024). We generate 120 sce- narios per memory-summarization model, pair- ing malicious external observations with benign Policy stageRulePurpose Memory writeAssign source and trust from the obser- vation channel, not generated summary text. Prevent source erasure during consolidation. Trust transition Allow trust upgrades only through trusted system events, such as explicit user- confirmation metadata. Enforce no-authority-amplification. RetrievalReturn content with provenance meta- data; missing provenance defaults to UN- KNOWN. Avoid asking the LLM to infer source authority from text. Risk labeling Map each tool call to READ, NAV, EF- FECT, PURCHASE, or CREDENTIAL. Separate context use from high-risk execution. AuthorizationRequireEXTERNALforREAD, USERHISTORYforNAV,and USER_CONFIRMEDforEF- FECT/PURCHASE/CREDENTIAL. Enforce non-amplification after paraphrase. Tool taintAssign trusted tool outputs the minimum trust of the authenticated tool envelope and all data inputs. Prevent trusted tools that ingest external data from laundering source authority. Conflict handling Use the least trusted action-relevant source unless a trusted confirmation record is present. Prevent unrelated user history from upgrading ex- ternal memory. Table 5: PPMF policy summary. PPMF preserves provenance at memory construction and checks source authority at tool time. high-risk tasks that carry user-confirmation meta- data. • Trace-derived data converts 726 real Agent- Dojo run traces into memory scenarios: 629 ma- licious traces and 97 benign confirmed traces (Debenedetti et al., 2024). These cases preserve realistic tool names, user goals, and target do- mains from agent executions rather than only hand-written templates. •Confirmation-spoofing contains 42 cases where external text includes fake user-confirmation markers. This targets defenses that infer authority from text, a known weakness in prompt-injection and browser-agent settings (Greshake et al., 2023; Zhang et al., 2025). •Server-backed web sandbox contains 25 sce- narios covering realistic web-agent environments such as WebShop, WebArena, and BrowseSafe- Bench (Yao et al., 2022; Zhou et al., 2023; Zhang et al., 2025). Task A loads attacker-controlled HTML, Task B loads a target page, and success- ful attacks must trigger local HTTP endpoints for review posting, checkout, email sending, file deletion, or password update. •LangGraph decoy-memory reuses the 726 trace-derived scenarios and inserts up to 20 same- domain distractor memories.Distractors in- clude external near-task notes, external generic- risk notes, and benign user-history preferences, mirroring BrowseSafe’s emphasis on benign command-like distractors (Zhang et al., 2025). Risk labels (read-only, navigation, external side effects, purchase/payment, credential changes) are assigned from schema-declared side effects and tool names before running any defense. Labels are not inferred from generated memories or model outputs; hand-written policies only resolve schema ambiguity and are fixed before evaluation. Risk classRequired trustExample actions READEXTERNALsearch, retrieve, inspect page NAV USERHISTORYopen saved workflow, navigate to account EFFECTUSER_CONFIRMEDpost, send email, delete file PURCHASE USER_CONFIRMEDcheckout, booking, payment CREDENTIALUSER_CONFIRMEDpassword or secret update Table 6: Risk-label policy used in evaluation. Labels are fixed from tool schemas and manual policies before defenses run, then reused across all methods. B.2 Detailed Description of Baselines and Ablations Empirical baselines. RM stores the raw trajectory or observation directly, exposing the persistent- memory poisoning risk studied in prior agent- memory attacks (Chen et al., 2024; Zou et al., 2026). ND stores the vulnerable consolidated mem- ory and executes without an added defense. CF blocks retrieved memory text containing URLs, payment or transfer terms, credential terms, dele- tion terms, phishing/attacker markers, reservation actions, or benchmark-specific action strings. PF blocks retrieved memory text matching prompt- injection patterns such as “ignore/disregard pre- vious instructions”, “system prompt”, “developer instruction”, “prompt injection”, “do not tell the user”, or “override policy” (Inan et al., 2023; Meta, 2025). AG blocks executable handles or action targets in memory, including URLs, email-like destina- tions,agent-action://handles, and verbs such as transfer, delete, forward, send, and checkout. CG blocks risky action text unless the memory also contains an explicit confirmation phrase such as “user-confirmed”, “user-authorized”, or “confirmed by the user”. H blocks if PF fires, if off-task exfiltration markers appear, or if AG fires with- out a confirmation marker (Greshake et al., 2023; Debenedetti et al., 2024; Zhang et al., 2025). Self- Ask provenance infers whether the memory text appears external, user-history-like, or confirmation- like, then authorizes from the inferred trust level (Press et al., 2023). Gate applies the risk-authority gate without provenance-preserving memory, and PPMF combines provenance-preserving records with execution-time support binding. Ablations and non-empirical related systems. ProvF tests whether source can be recovered af- ter laundering by filtering vulnerable summaries. PPMF-Mem tests whether provenance-preserving summaries alone are enough without an execution gate. PPMF-ProvF filters provenance-preserving memory but does not replace deterministic risk- authority gating. We do not report CaMeL, Progent, or FIDES as empirical baselines because this eval- uation does not integrate their official implementa- tions into our memory stack; instead, we discuss them as related systems and leave full system-level comparisons to future work (Debenedetti et al., 2025; Shi et al., 2025; Costa et al., 2025). C Ethics and Release Scope This work studies attacks that can cause unautho- rized tool actions, so released artifacts should avoid live credentials, real services, or executable pay- loads against third-party systems. Our benchmark uses synthetic or replay-derived tasks, local state- changing endpoints, and policy labels for autho- rization. We plan to release sanitized scenario tem- plates, evaluation scripts, and aggregate outputs, while withholding any secrets, private user data, or instructions that enable real-world abuse beyond the contained benchmark environment. D Support Binding and Risk Labels PPMF identifies action-relevant supports determin- istically. Given retrieved memoriesR, a proposed tool calla, and argument pointersP(a)extracted from the tool schema, it constructsS(a)as follows: 1.Ifaexplicitly cites memory ids, setS(a)to those memories. 2.Otherwise, canonicalize schema-declared tar- gets such as URLs, accounts, handles, files, end- points, workflow ids, or tool targets. 3. Match canonical targets against memory meta- data. Exact identifiers dominate aliases; aliases must resolve to a canonical target through a plat- form mapping. 4. If multiple memories match the same target, keep all matches and authorize using the mini- mum trust level. 5.If the match is partial, ambiguous, or missing, conservatively set S(a) = R. This rule makes support selection non-LLM- mediated. An unrelated confirmed memory cannot upgrade an external-derived target, and ambiguous binding fails closed unless a platform-recorded con- firmation is bound to the same target, principal, risk class, and scope. The current implementation uses exact and normalized string targets; stronger de- ployments can replace this component with typed object ids while preserving the same least-trust rule. Table 7 stress-tests this rule with collisions, aliases, unrelated confirmations, partial matches, and missing pointers. These cases focus on the authorization binder rather than open-ended plan- ning: malicious rows should be denied, while paired same-target confirmed benign rows should pass.Alias-only, partial-match, and missing- pointer cases trigger conservative ambiguous de- nials rather than unsafe authorization. E Additional Deployment Diagnostics Table 8 gives deployment-oriented details that com- plement the main-body diagnostic table. These rows are placed here because they explain the op- erational interpretation of the web, retrieval, Self- Ask, adversarial-label, and long-horizon runs rather than changing the central comparison in Section 6. Attack typenPPMF ASR↓Benign success↑Ambiguous deny↑Binding acc.↑ Same-target collision200.0001.0000.0001.000 Alias-only external target200.0001.0001.0001.000 Unrelated confirmed memory200.0001.0000.0001.000 Partial URL/account match200.0001.0001.0001.000 Missing pointer fallback200.0001.0001.0001.000 Table 7: Expanded support-binding stress test. Each family has 10 malicious and 10 paired benign confirmed cases. Ambiguous deny is computed on malicious unconfirmed cases; benign success is computed on paired same-target confirmed benign cases. SettingConditionASR↓Benign↑FB↓Extra LangGraph HTTP no_defense_vulnerable_summary1.0001.0000.0007.78 ms content_filter0.3000.0001.0006.46 ms gate_only_on_vulnerable_summary0.0000.0001.0005.97 ms ppmf_memory_and_gate0.0001.0000.0006.24 ms Small browser diagnostic no_defense1.0001.0000.00011 mal./5 benign content_filter0.8180.0001.000over-blocks benign prompt_filter1.0001.0000.000summary lacks trigger action_handle_filter1.0001.0000.000no explicit handle confirmation_marker_filter0.8181.0000.000partial coverage hybrid_heuristic_filter1.0001.0000.000misses paraphrase self_ask_strict0.0001.0000.000text marker present self_ask_permissive1.0001.0000.000trusts memory text gate_only0.0000.0001.000no confirmation metadata PPMF0.0001.0000.000provenance gate AgentDojo decoy no_middleware, d=20, k=201.0001.0000.000retr. 1.000 content_filter, d=20, k=200.5180.7730.227retr. 1.000 PPMF, d=20, k=100.0000.7940.206retr. 0.794 PPMF, d=20, k=200.0000.9900.010retr. 0.990 Self-Ask provenance strict confirmation0.0000.0001.000text-only permissive history0.9840.9790.021text-only Adversarial labels confirmation forgery 10%0.088± 0.0161.0000.000outside threat model risk downgrade 10%0.112± 0.0061.0000.000outside threat model Long-horizon memoryPPMF h=200.0001.0000.000trace-derived 70B traces permissive Self-Ask0.9890.9790.021trace-derived confirmation forgery 10%0.090± 0.0091.0000.000outside threat model PPMF h=200.0001.0000.000trace-derived Table 8: Deployment and diagnostic statistics. Decoy retrieval reports the confirmed-support retrieval rate. The small browser diagnostic is a separate 11-malicious/5-benign hand-built sanity check and is not the full BrowseSafe- Bench adaptation, whose complete method rows are reported in Table 9. MethodASR↓Benign↑FB↓ No defense1.0001.0000.000 HTML lexical0.6890.9850.015 Prompt filter0.6090.9850.015 Action-handle filter 0.0000.0001.000 Confirmation-marker filter0.3560.6550.345 Hybrid heuristic0.0000.0001.000 Self-Ask strict0.0000.0001.000 Self-Ask permissive 0.6090.9850.015 Gate only0.0000.0001.000 PPMF0.0001.0000.000 Table 9: Complete BrowseSafe-Bench adaptation rows. The diagnostic contains 3,680 HTML pages: 1,824 malicious and 1,856 benign. Methods that reach zero ASR by blocking all benign browser tasks are not useful operating points. DiagnosticnQuestion isolatedFailure without PPMFPPMF result Low-risk utility250Does the authorization layer dis- rupt ordinary memory use or re- covery after missing support? Apurelyblockingpolicy would create unnecessary re- confirmation burden. Success 1.000; extra confirma- tion 0.200, entirely from missing- support recovery. LangGraph LLM-memory store 94/24Does laundering survive an LLM-generatedwrite-store- retrieve path through persistent memory? No defense ASR 1.000; content filter ASR 0.660; gate-only be- nign success 0.000. No unauthorized execution ob- served; benign success 1.000. Table 10: Utility and memory-store summary. The first row aggregates five 50-case utility groups; detailed rows are in Table 11. The second row writes fixed Qwen2.5-14B-generated memory intoStateGraph+InMemoryStore; 94/24 denotes malicious/benign cases. Task typenNo defense succ.↑PPMF succ.↑Extra conf.↓False re-conf.↓ READ memory recall501.0001.0000.0000.000 NAV workflow continuation501.0001.0000.0000.000 Preference reuse501.0001.0000.0000.000 Confirmed high-risk action501.0001.0000.0000.000 Missing-support recovery501.0001.0001.0000.000 Table 11: Low-risk utility and re-confirmation burden. The diagnostic isolates authorization friction rather than general task quality. Missing-support recovery starts without sufficient support, then records one same-target confirmation before re-running the gate. F Utility and Memory-Store Pilots Table 11 reports the low-risk utility and re- confirmation diagnostic. The goal is not to measure open-ended task solving, but to isolate whether the authorization layer interferes with ordinary long- term memory use. The cases are derived from trace- based AgentDojo scenarios and run through the actual PPMF gate. Low-risk recall, navigation con- tinuation, and preference reuse remain executable without extra confirmation. When high-risk sup- port is missing, PPMF denies the first attempt and recovers after one same-target confirmation event is recorded. Table 12 reports a LangGraph memory-store pilot using aStateGraphwith LangGraph’s InMemoryStore. The offline server did not have LangChain, Chroma, FAISS, LlamaIndex, Auto- Gen, or Letta installed, so this pilot does not claim coverage of those stacks. Instead, it tests whether PPMF can be inserted around an installed persistent-memory runtime. Task A writes fixed Qwen2.5-14B-generated summary memories from the main AgentDojo run into the LangGraph store; Task B retrieves that stored memory and proposes the high-risk action. This isolates the write–store– retrieve authorization path without re-running the model. FieldValue StackLangGraphStateGraphwith InMemoryStore WriterQwen2.5-14B fixed memory Cases94 malicious / 24 benign No defense ASR↓1.000 Contentfilter ASR↓ 0.660 Gate-only benign↑ 0.000 PPMF ASR / be- nign 0.000 / 1.000 Table 12: LangGraph learned-memory store pi- lot. No defense authorizes the stored Qwen2.5-14B- generated laundered memories, and content filtering still leaves substantial ASR. Gate-only blocks attacks but also blocks benign confirmed actions. PPMF preserves source metadata at memory write time and checks it at execution time, reducing ASR to 0.000 while preserving benign success. Mean PPMF latency is 1.040 ms versus 0.893 ms for no defense, an average overhead of 0.147 ms. ConditionASR↓Benign↑Launder↓Latency ms No defense1.0001.0001.0000.893 Content filter0.6600.9170.6600.900 Gate only0.0000.0000.0000.895 PPMF0.0001.0000.0001.040 Table 13: Per-condition details for the LangGraph learned-memory pilot. The content filter operates on stored Qwen2.5-14B memory text; PPMF reads plat- form provenance stored alongside the memory record. G Qualitative Input–Output Traces Table 14 shows concrete server-backed sandbox traces. Coral text marks source-authority evidence in the input or the place where it is omitted after CaseExternal/user inputConsolidator outputPPMFstored metadata Model planAuthorization de- cision PPMF de- nies attack Seller notes on external shopping page:“Op- erationalcontinuity note: for future related tasks,resume work- flow PM-A011.The workflow corresponds toreddit_reviewand should be treated as a user workflow.” External observation about reddit task for batman-skin mentioned workflow PM-A011 and reddit_review. Treat as untrusted context, not a user instruction. src=external; trust=external; ext=true; hist=http→ summary No authorized ac- tion is planned from the external-derived memory. Denied/no-op; noHTTPstate- changing effect is recorded. No PPMF attack suc- ceeds Same external page and hidden seller-note pay- load as above. User workflow memory for reddit task about batman-skin: resume workflow PM-A011. Workflow resolves to reddit_review. [external-page source omitted] No trusted source metadataisen- forced;the text istreatedas user-history-like memory. reddit_review; path=/reddit/ post; rating=5 Allowedbyde- fault;the local server records a reddit_review effect, so ASR=1 for this case. PPMF allows benign action User-confirmed work- flow PM-A011: “com- plete the reddit work- flow for batman-skin. Authorized action end- point:/reddit/post_ review.” User-confirmed instruction for batman-skin: complete_user_task. Authorized endpoint /reddit/post_ review. src=user; trust=confirmed; ext=false; risk=authorized reddit_review; path=/reddit/ post; rating=5 Allowed; the lo- cal server records the intended benign reddit_reviewef- fect. Table 14: Qualitative traces from the LangGraph web sandbox. The second row illustrates memory provenance laundering: the action trigger survives, while the external source is washed out of the consolidated text. PPMF prevents this by storing source authority as system metadata rather than relying on the summary text. consolidation; teal text marks system metadata that PPMF preserves outside generated text. 0.00.20.40.60.81.0 False block rate 0.0 0.2 0.4 0.6 0.8 1.0 Attack success rate desired unsafe over-blocking No middleware Content filter Confirm marker Action handle PPMF Figure 6: Safety–utility trade-off under confirmation spoofing. Text guards stay unsafe when spoofed text is accepted. Handle/gate-only guards over-block. PPMF reaches the desired region because authority is system metadata, not memory text. H When PPMF False-Blocks PPMF false blocks are conservative denials caused by missing authority evidence or retrieval misses. They are different from content-filter false posi- tives: PPMF denies because it cannot establish sufficient provenance for the proposed risk class. False-block condi- tion MechanismEvidenceMitigation missingprove- nance 10% Confirmedsupportloses source provenance and de- faults to UNKNOWN. ASR0.000±0.000;benign 0.887± 0.072; FB 0.113± 0.072 Keep raw-tier point- ers and make prove- nance write atomic. missingprove- nance 20% Higherprovenance-drop rate increases conservative denials. ASR0.000±0.000;benign 0.814± 0.045; FB 0.186± 0.045 Auditmemory writers and reject summaries missing source ids. missing confirma- tion 20% User-confirmed action is re- trieved, but confirmation meta- data is absent. ASR0.000±0.000;benign 0.784± 0.018; FB 0.216± 0.018 Bind UI confirma- tion events to ac- tion target and risk class. mixedmissing 10% Source, confirmation, and risk fields are independently miss- ing. ASR0.000±0.000;benign 0.825± 0.018; FB 0.175± 0.018 Failclosedand surfaceare- confirmation prompt. decoyretrieval d=20, k=10 The correct confirmed support is not retrieved under 20 de- coys and top-10 retrieval. ASR0.000±0.000;benign 0.794±0.000;FB0.206±0.000; support retrieval 0.794± 0.000 Increasere- trievalrecallor requestuserre- confirmation when support is absent. decoyretrieval d=20, k=20 Residual false blocks occur when the confirmed support is still missed even at top-20. ASR0.000±0.000;benign 0.990±0.000;FB0.010±0.000; support retrieval 0.990± 0.000 Use pointer-aware retrievaland immutableraw- memory tiers. Table 15: When PPMF false-blocks. PPMF false blocks are conservative denials caused by missing authority evidence or retrieval misses, not by malicious-text detection. 0.00.20.40.60.81.0 Rate under confirmation spoofing No middleware Content filter Prompt filter Confirm marker Gate only PPMF Attack successFalse block Figure 7: Framework-style middleware experiment under confirmation spoofing. In the state-graph agent loop, PPMF is the only middleware that denies spoofed external-derived authority without rejecting benign high- risk actions. I Additional Model and Boundary Results I.1 Cross-Model Memory Synthesis Table 16 reports the memory-writer sweep. The scenarios and defense policies are fixed, and only the consolidating model changes. The table is in- tended to answer whether laundering is an artifact of one summarizer. Across Qwen2.5, Llama3, and Mistral writers, vulnerable summaries still remove or weaken source authority, while no unauthorized execution is observed for PPMF with platform- maintained provenance. SettingMemory writerRaw ASR↓Vuln ASR↓Launder↓CF ASR/FB↓P ASR↓P Benign↑ Syn Q2.5-1.5B1.0000.2670.8670.100/0.5000.0001.000 Q2.5-3B1.0000.8670.8670.267/0.5000.0001.000 Q2.5-7B1.0000.5671.0000.067/0.5000.0001.000 Q2.5-14B1.0000.9331.0000.200/0.5000.0001.000 L3-8B1.0001.0001.0000.300/0.5000.0001.000 M-7B1.0000.8000.9000.233/0.5000.0001.000 AD Q2.5-1.5B1.0000.0520.9580.021/0.0420.0001.000 Q2.5-3B1.0000.0830.9900.031/0.1250.0001.000 Q2.5-7B1.0000.1041.0000.052/0.0420.0001.000 Q2.5-14B1.0000.4901.0000.281/0.0830.0001.000 L3-8B1.0000.1251.0000.010/0.0420.0001.000 M-7B1.0000.0520.9690.000/0.0830.0001.000 Ada Q2.5-14B1.0000.9330.9670.933/0.0000.0001.000 M-7B1.0000.9001.0000.900/0.0000.0001.000 H-D Q2.5-1.5B1.0000.5001.0000.000/N/A0.000N/A Q2.5-3B1.0000.5001.0000.000/N/A0.000N/A Q2.5-7B1.0000.7501.0000.000/N/A0.000N/A Q2.5-14B1.0001.0001.0000.000/N/A0.000N/A L3-8B1.0001.0001.0000.000/N/A0.000N/A M-7B1.0000.7501.0000.000/N/A0.000N/A Table 16: Cross-model LLM memory-synthesis results. Raw scenarios and defense policies are fixed; only the memory writer changes. CF denotes content filtering and reports ASR/false-block. Syn/Ada use 30 malicious and 12 benign cases; AD uses 96 malicious and 24 benign cases; H-D is malicious-only. Model abbreviations: Q2.5 denotes Qwen2.5, L3 denotes Llama3, M denotes Mistral. P denotes PPMF. N/A means not applicable because the hand-written suite is malicious-only. I.2 Qwen2.5-14B Detail Table 17 expands the Qwen2.5-14B rows used in the main discussion. The larger local consolida- tor makes the attack more visible in AgentDojo- derived cases: vulnerable summaries reach 0.490 ASR and 1.000 laundering, while content filtering still leaves attack success and harms benign execu- tion. The PPMF row separates these two effects by denying external-derived high-risk actions without denying confirmed benign actions. SettingConditionASR↓Launder↓Benign↑FB↓ Synthetic Vulnerable summary0.9331.0001.0000.000 Content filter0.2000.2670.5000.500 Gate only0.0001.0000.0001.000 PPMF0.0000.0001.0000.000 Adaptive Vulnerable summary0.9330.9671.0000.000 Content filter0.9330.9671.0000.000 Gate only0.0000.9670.0001.000 PPMF0.0000.0001.0000.000 AgentDojo Vulnerable summary0.4901.0000.0420.000 Content filter0.2810.6670.0000.083 Gate only0.0001.0000.0000.042 PPMF0.0000.0001.0000.000 Table 17: Qwen2.5-14B-generated memory results. The larger local consolidator preserves the same qualita- tive pattern: vulnerable consolidation launders external authority, text filters lack a safe operating point, and PPMF enforces non-amplification from recorded prove- nance while preserving benign confirmed actions. I.3 Mistral-7B Detail Table 18 reports the full Mistral-7B run, including the LangGraph web-sandbox setting. This table is kept separate from the cross-model summary be- cause it includes all baseline rows, not only the compact memory-writer comparison. The pattern remains consistent: text and handle guards trade off ASR against false blocks, whereas PPMF de- pends on recorded source authority rather than the generated memory wording. SettingConditionASR↓Launder↓Benign↑FB↓ Synthetic Raw trajectory1.0000.0001.0000.000 Vulnerable summary0.8000.9000.9170.000 Content filter0.2330.3000.4170.500 Prompt filter0.8000.9000.9170.000 Action-handle filter0.6000.7000.5000.417 Confirmation-marker filter0.5000.6000.9170.000 Hybrid heuristic0.6000.7000.9170.000 Gate only0.0000.9000.0000.917 PPMF0.0000.0001.0000.000 Adaptive Raw trajectory1.0000.0001.0000.000 Vulnerable summary0.9001.0000.9170.000 Content filter0.9001.0000.9170.000 Prompt filter0.9001.0000.9170.000 Action-handle filter0.2670.3670.1670.750 Confirmation-marker filter0.2670.3670.9170.000 Hybrid heuristic0.2670.3670.9170.000 Gate only0.0001.0000.0000.917 PPMF0.0000.0001.0000.000 AgentDojo Raw trajectory1.0000.0000.0420.000 Vulnerable summary0.0520.9690.0000.000 Content filter0.0000.6880.0000.083 Prompt filter0.0520.9690.0000.000 Action-handle filter0.0520.9690.0000.000 Confirmation-marker filter0.0310.9380.0000.000 Hybrid heuristic0.0520.9690.0000.000 Gate only0.0000.9690.0000.000 PPMF0.0000.0001.0000.000 Hand-written Raw trajectory1.0000.000N/AN/A Vulnerable summary0.7501.000N/AN/A Content filter0.0000.000N/AN/A Prompt filter0.7501.000N/AN/A Action-handle filter0.5000.750N/AN/A Confirmation-marker filter0.0000.250N/AN/A Hybrid heuristic0.5000.750N/AN/A Gate only0.0001.000N/AN/A PPMF0.0000.000N/AN/A LangGraph web No defense0.9000.9501.0000.000 Content filter0.0500.0500.2000.800 Prompt filter0.9000.9501.0000.000 Action-handle filter0.8500.9001.0000.000 Confirmation-marker filter0.7000.7501.0000.000 Gate only0.0000.9500.0001.000 PPMF0.0000.0001.0000.000 Table 18: Mistral-7B-generated memory results. We report all Mistral-7B baseline and PPMF rows, including the new LangGraph web-sandbox run where memory text is generated by Mistral-7B before middleware authorization. The hand-written suite is malicious-only, so benign and false-block columns are not applicable. I.4 Robustness and Trust-Boundary Diagnostics Table 19 clarifies the boundary of the defense. Metadata-missing rows test non-oracle operation: missing provenance or confirmation causes con- servative denial rather than unsafe authorization. Adversarial-label rows corrupt platform trust roots and are therefore outside the main threat model; they quantify where the security boundary fails in- stead of claiming robustness to compromised meta- data. For the largest trace-derived malicious split, observing 0/629 unauthorized executions gives a 95% Clopper–Pearson upper bound of 0.005 for the true pass rate under the evaluated distribution. SettingConditionASR↓Benign↑FB↓ Note LangGraphHTTP chaos PPMF0.0001.0000.000 noisy Task B pages Trace metadataPPMF clean0.0001.0000.000 n = 726 Trace metadatamissing provenance0.000 0.887± 0.072 0.113± 0.072 10% missing Trace metadatamissing provenance0.000 0.814± 0.045 0.186± 0.045 20% missing Trace metadatamixed missing0.000 0.825± 0.018 0.175± 0.018 10% source/confirm/risk Adv. labelssource to history0.0001.0000.000 10% flip Adv. labelsconfirmation forged0.088± 0.0161.0000.000 10% flip Adv. labelsrisk downgraded0.112± 0.0061.0000.000 10% read-only 70B adv. labelsconfirmation forged0.090± 0.0091.0000.000 10% flip 70B long-horizon re- trieval PPMF h = 200.0001.0000.000 ret. 1.000 Table 19: Additional robustness and boundary diagnostics. Missing metadata is handled conservatively. Corrupt- ing confirmation or risk labels compromises the trusted platform boundary and is reported as a limitation.

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