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SINGED: Correct Outputs Do Not Certify Safe Execution in LLM Agents
Xiaoyu Xu, Zi Liang, Minxin Du, Qipeng Xie, Qingqing Ye, Yuyuan Li, Haibo Hu
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 91%
Last extracted: 9/30/2026, 6:55:23 AM
Summary
The paper introduces SINGED, a benchmark designed to evaluate the security of LLM agents when selecting third-party tools. It demonstrates that agents often execute 'functional counterfeits'—malicious implementations that produce correct outputs but have hidden side effects—particularly when these counterfeits are ranked first. The study highlights a significant gap between output correctness and execution safety, showing that standard evaluations missing process traces fail to detect these risks.
Entities (8)
Relation Signals (5)
SINGED → detectsthreat → Functional Counterfeits
confidence 97% · SINGED... exposes a rank-, evidence-, and choice-sensitive outcome-to-execution gap... functional counterfeits: implementations that match benign alternatives... but add an effect forbidden by the task contract.
Correct Outputs → donotcertify → Safe Execution
confidence 96% · Correct Outputs Do Not Certify Safe Execution in LLM Agents... evaluation must connect correct outputs to execution paths.
Rank 1 → increasesriskof → Counterfeit Execution
confidence 95% · the randomized-rank study finds counterfeit execution in 45% (27/60) of rank-one trials and none at later ranks.
Cross-candidate comparison → reduces → Layered Failures
confidence 90% · Cross-candidate comparison eliminates shallow failures and reduces layered failures from 15.7% to 4.2%
Privacy Filter incident → illustratesrisk → Source Selection Problem
confidence 88% · The Privacy Filter incident illustrates this risk... This exposes a source-selection problem
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Abstract
Abstract:Tool-using language-model agents select and execute third-party artifacts. Different implementations can return the requested output while producing hidden execution effects that task-, attack-, or choice-based evaluations may miss. We study functional counterfeits: implementations that match benign alternatives on the requested output but add an effect forbidden by the task contract. We introduce SINGED (Source Integrity and the Nonidentifiability Gap in Execution Decisions for LLM Agents), a controlled benchmark covering five primary and two held-out task families. It varies displayed rank, evidence depth, decision policy, model release, and agent configuration, while task and process oracles verify the artifact and execution path. Across 7,549 audited trials, the randomized-rank study finds counterfeit execution in 45% (27/60) of rank-one trials and none at later ranks. Cross-candidate comparison eliminates shallow failures and reduces layered failures from 15.7% to 4.2%, but leaves dependency failures; its benefit is uncertain on unseen effects and public-package structures. Moreover, seven releases with no counterfeit executions when benign alternatives are available execute the counterfeit in 55/175 single-source cells after alternatives are removed. SINGED thus exposes a rank-, evidence-, and choice-sensitive outcome-to-execution gap: evaluation must connect correct outputs to execution paths.
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- Source: https://arxiv.org/abs/2609.35889v1
- Canonical: https://arxiv.org/abs/2609.35889v1
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SINGED: Correct Outputs Do Not Certify Safe Execution in LLM Agents Xiaoyu Xu 1† Zi Liang 1† Minxin Du 1* Qipeng Xie 1 Qingqing Ye 1 Yuyuan Li 2 Haibo Hu 1* 1 The Hong Kong Polytechnic University 2 Hangzhou Dianzi University † Equal contribution * Corresponding authors xiaoyu0910.xu@connect.polyu.hkminxin.du, haibo.hu@polyu.edu.hk Tool-using language-model agents select and execute third-party artifacts. Different implementations can return the requested output while producing hidden execution effects that task-, attack-, or choice-based evaluations may miss. We study functional counterfeits: implementations that match benign alternatives on the requested output but add an effect forbidden by the task contract. We introduce SINGED (Source Integrity and the Nonidentifiability Gap in Execution Decisions for LLM Agents), a controlled benchmark covering five primary and two held-out task families. It varies displayed rank, evidence depth, decision policy, model release, and agent configuration, while task and process oracles verify the artifact and execution path. Across 7,549 audited trials, the randomized-rank study finds counterfeit execution in 45% (27/60) of rank-one trials and none at later ranks. Cross-candidate comparison eliminates shallow failures and reduces layered failures from 15.7% to 4.2%, but leaves dependency failures; its benefit is uncertain on unseen effects and public-package structures. Moreover, seven releases with no counterfeit executions when benign alternatives are available execute the counterfeit in 55/175 single-source cells after alternatives are removed. SINGED thus exposes a rank-, evidence-, and choice-sensitive outcome-to-execution gap: evaluation must connect correct outputs to execution paths. GitHub RepositoryProject Website 1 Introduction Language-model agents extend language models from generating responses to acting in an environment. They interpret observations, select tools or resources, execute actions, and use feedback to continue or terminate. This loop supports reasoning and acting (Yao et al., 2023), API use (Schick et al., 2023), and selection from large tool registries (Patil et al., 2024, 2025). Coding runtimes operationalize the loop through persistent state, tool interfaces, and termination logic (OpenAI, 2026a; Earendil Works, 2026). Interactive benchmarks evaluate execution trajectories across websites, computers, repositories, and stateful services (Zhou et al., 2024; Mialon et al., 2024). This shift changes what evaluation must certify. An agent may return a correctly redacted file while the selected implementation also transmits the unredacted input, a behavior absent from the artifact. The Privacy Filter incident illustrates this risk: OpenAI releasedopenai/privacy-filterfor local personal-information detection and redaction, while another Hugging Face repository copied its model card and functionality but loaded an information stealer before its removal (OpenAI, 2026c; HiddenLayer Research Team, 2026). Neither a malicious request nor an adversarial instruction was required, only a plausible compromised source. This exposes a source-selection problem: When implementations advertise the same function and produce the same output, which source does an agent execute, and how 1 arXiv:2609.35889v1 [cs.CR] 27 Sep 2026 Table 1: Representative evaluation designs at the agent–artifact boundary. CitationEvaluation settingInterventionMatched outputEndpointRandom rank Greshake et al. (2023)Indirect prompt injectionExternal instruction×Attack success× Shi et al. (2025)Tool metadata poisoningTool description×Tool selection× Hu et al. (2026)Malicious tool generationImplementation×Attack success× Sneh et al. (2025)Tool-surface optimizationTool interface×Tool selection× Qu et al. (2026)Agent-skill supply chainSkill code×Attack success× Bito et al. (2025)Position-bias evaluationList orderN/ARecommendation✓ This workSINGEDImplementation + order✓Output + events✓ do rank, inspectable evidence, and decision policy change that choice? This problem connects supply-chain security with agent security. Supply-chain research documents malicious packages, dependency confusion, and model-hub loader risks (Ohm et al., 2020; Ladisa et al., 2023; Casey et al., 2024), while agent-security research studies external instructions, poisoned tool descriptions, memories, skills, and unsafe plans (Greshake et al., 2023; Debenedetti et al., 2024; Liu et al., 2026b,a). Yet existing evaluations do not jointly hold the benign request and expected output fixed while varying the implementation, randomizing its displayed rank, and recording process effects (Table 1). Consequently, an instruction-free, inspectable source can pass the task oracle while producing forbidden behavior that output-only and inspection-only evaluation misses. Motivated by this gap, we introduce SINGED (Source Integrity and the Nonidentifiability Gap in Execution Decisions for LLM Agents), a controlled evaluation framework inspired by the source ambiguity exposed by the Privacy Filter incident. Each instance pairs an ordinary request with three similarly named implementations: an intended source, a benign community alternative, and a functional counterfeit. All advertise the same capability, expose the same interface, and produce the expected artifact, but only the counterfeit adds an effect forbidden by the task contract. We vary the decisive code’s dependency depth and apply the construction to document redaction, speech transcription, paper retrieval, archive extraction, and tabular conversion. To isolate source selection, SINGED varies rank, evidence depth, decision policy, model release, and agent configuration while holding the request, advertised capability, interface, and expected artifact fixed. Candidate code remains inspectable before installation, no adversarial instruction appears in tasks or sources, and role labels remain hidden. As illustrated in Figure 1, a task oracle verifies returned artifacts, while a channel-separated process oracle records the executed implementation and its observable effects inside a network-isolated sandbox. This design separates output correctness from process integrity and identifies where a successful trajectory becomes compromised. Across 7,549 audited trials spanning 20 releases from 14 model families and 8 agent configurations, we find three patterns. First, correct outputs conceal rank-sensitive process failures. All outputs pass in the randomized-rank study, yet counterfeit execution occurs only at rank one; the replication preserves the gradient with one rank-two failure. Second, early commitment drives shallow failures. Mandatory comparison eliminates matched failures when decisive evidence is visible. Yet seven releases with zero counterfeit execution rate (CER) under three-source choice execute the counterfeit in 55/175 single-source cells after benign alternatives are removed. Third, comparison reduces but does not eliminate risk. Deeper dependencies, release changes, and unseen process effects expose its limits, showing that observed safety depends jointly on the evidence, alternatives, and system configuration across evaluated settings. We summarize our contributions as follows. I) Problem formulation. We identify source selection among implementations with equivalent benchmark outputs as an agent security problem, define functional counterfeits, and formalize CER, safe utility, and the outcome-to-execution gap missed by output-only evaluation. I) Benchmark and evaluation framework. We introduce SINGED, a controlled benchmark covering five primary and two held-out task families, plus a frozen public-package transfer slice. It holds requests, interfaces, and artifacts fixed while varying rank, evidence depth, decision policy, model release, and agent configuration; task and process oracles score both channels per trajectory. 2 I) Empirical findings. Using matched decision-rule interventions, we identify early commitment as a candidate mechanism for shallow failures and characterize when additional inspection changes source selection, including a 45-point rank-one CER reduction. Single-source and transfer tests define the boundary: observed safety depends on available alternatives and does not reliably generalize across deeper dependencies, releases, configurations, unseen effects, or public-package structures. 2 Related Work Tool-using agents and agent security. Tool-use research studies how language models interleave reasoning with external actions, retrieve APIs, and select among large tool collections (Yao et al., 2023; Schick et al., 2023; Patil et al., 2024). Interactive benchmarks extend this evaluation to websites, computers, repositories, and stateful services (Liu et al., 2024; Zhou et al., 2024; Xie et al., 2024; Yao et al., 2025), primarily scoring whether the agent reaches the requested state. Agent-security work examines indirect prompt injection, poisoned tool descriptions, malicious skills and implementations, and unsafe plans (Greshake et al., 2023; Debenedetti et al., 2024; Shi et al., 2025; Hu et al., 2026; Qu et al., 2026). These settings show how adversarial content redirects agents, but not how agents choose among output-equivalent sources for the same benign request. Software supply chains and source selection. Supply-chain studies document malicious packages, dependency confusion, hallucinated dependencies, and executable model loaders (Ohm et al., 2020; Ladisa et al., 2023; Spracklen et al., 2025; Casey et al., 2024). Position-bias studies separately show that list order can alter model choices (Bito et al., 2025; Zhang et al., 2026a; Khan et al., 2026). As Table 1 summarizes, prior designs do not jointly hold the benign request and expected output fixed, vary the implementation, randomize its displayed rank, and observe process effects. SINGED connects these literatures by measuring source choice and execution integrity under output-equivalent alternatives across matched, auditable agent trajectories; Appendix C expands this comparison. 3 Problem Setting Agent trials. Following interactive evaluations that model agents through alternating actions and observations (Yao et al., 2023; Liu et al., 2024), each trial asks an agent to select and execute one candidate source. A source is an implementation claiming to perform the requested task. The agent comprises a language model푀that proposes actions, an agent configuration퐹that maintains state and exposes tools, a prompted rule푝that guides inspection and selection, and an optional controller푔that may block actions before execution. A task contract푐 푗 = (푞 푗 , 푋 푗 ,푉 푗 , 퐵 푗 )specifies user request푞 푗 , frozen validation inputs푋 푗 , task oracle푉 푗 , and forbidden process events퐵 푗 . A benchmark instance푗 = (푐 푗 ,푥 푗 )selects input푥 푗 ∈ 푋 푗 . Before agent evaluation, we run every candidate on푋 푗 to establish identical outputs and separate benign from forbidden process behavior. An evaluation unit푖 = (푗 푖 ,A 푖 , 푀 푖 , 퐹 푖 ,푒 푖 )fixes instance푗 푖 , candidate setA 푖 , model푀 푖 , agent configuration 퐹 푖 , and a network-isolated sandbox푒 푖 .푞 푖 ,푥 푖 ,푉 푖 ,and퐵 푖 denote its request, input, task oracle, and forbidden- event set. A trial evaluates it under휃 = (휎, 푝, 푑,푔):휎orders candidates,푝sets decision rule,푑locates decisive code at entrypoint or in dependencies, and푔sets the controller. Assigned depth locates evidence; observed depth measures how far the agent reads. During execution, the model proposes actions from history,푔permits or blocks them, and the sandbox returns observations. Default controller푔 0 permits all actions. Trace 휏 푖 (휃) = (ℎ 푖 ,푎 exec,푖 , 푦 푖 ) records history, executed hash, and output. Non-execution sets 푎 exec,푖 =⊥; missing output sets 푦 푖 =⊥. Functional counterfeits. For contract푐 푗 = (푞 푗 , 푋 푗 ,푉 푗 , 퐵 푗 ), two implementations are benchmark output equivalent if both terminate, return identical outputs, and pass푉 푗 for every푥 ∈ 푋 푗 . Candidate푎 푐 is a functional counterfeit for(푐 푗 ,푒)if it is benchmark output equivalent to every benign candidate but triggers an event in퐵 푗 on every validation input in environment푒. Benign candidates must trigger no event in퐵 푗 . This claim applies only to the frozen inputs and does not imply equivalence elsewhere. 3 Because either benign candidate satisfies both task and process requirements, evaluation depends on execution behavior rather than a private “official” label. Two-channel evaluation. Agent-security benchmarks commonly score task outcomes or attack suc- cess (Debenedetti et al., 2024; Ruan et al., 2024). We instead separate the returned artifact from the process that produced it. Task oracle푉 푖 (푥 푖 , 푦 푖 ) ∈ 0, 1accepts a correct output. The process channel records sandbox eventsEvents(휏 푖 )and checks whether any belong to퐵 푖 . Across our tasks, forbidden effects include external transmission, out-of-scope writes, unrelated-file reads, unauthorized metadata changes, and unnecessary child processes. We define 푇 푖 = 1푉 푖 (푥 푖 , 푦 푖 ) = 1, 퐻 푖 = 1Events(휏 푖 )∩ 퐵 푖 ≠∅, 푆푈 푖 = 푇 푖 (1− 퐻 푖 ).(1) Let1·denote the indicator function.푇 푖 is task success,퐻 푖 a forbidden process effect, and푆푈 푖 success without one. In accepted instances, only executing the counterfeit triggers the designated event by construction, so퐻 푖 also identifies counterfeit execution within SINGED. For analyzable trialsI 휃 under condition 휃, let 푁 휃 = |I 휃 |. We define CER(휃) = 1 푁 휃 ∑︁ 푖∈I 휃 퐻 푖 (휃),OEG(휃) = 1 푁 휃 ∑︁ 푖∈I 휃 푇 푖 (휃)퐻 푖 (휃) =푇(휃)−푆푈(휃).(2) Overbars denote means overI 휃 . Counterfeit execution rate (CER) measures how often the counterfeit runs. The outcome-to-execution gap (OEG) is the share of correct outputs produced through compromised execution, equal to how much task success overstates safe utility in that condition. Proposition 1 (Nonidentifiability under output equivalence). Let휏 푏 and휏 푐 be benign and compromised traces for the same contract푐 푗 and input푥 ∈ 푋 푗 . Suppose both return푦and satisfy푉 푗 (푥, 푦) = 1, while퐻(휏 푏 ) = 0and퐻(휏 푐 ) = 1. Any possibly randomized evaluator observing only(푞 푗 ,푥, 푦)produces the same score distribution for both traces. Under an equal prior, no output-only rule identifies the compromised trace with accuracy greater than 1/2. Proof. Both traces yield the same(푞,푥, 푦), hence identical scores and the stated bound.□ The gap is structural and cannot be identified without process evidence. Threat model and contrasts. Subject to the matching constraints in Section 4, the adversary controls one discoverable candidate’s name, metadata, code, and dependencies. The adversary cannot modify the request, trial input, benign candidates, expected artifact, sandbox, or oracle logs. All candidate code is inspectable before execution. Thus, hidden means absent from the returned artifact, not invisible in source or process logs. The task oracle observes the request, input, artifact, and expected hash; the process oracle observes the executed hash and sandbox event log. These channels are separate, although their outcomes need not be statistically independent. We place the counterfeit equally often at three displayed ranks while keeping the benign candidates’ relative order fixed. Let ̄ 퐻(푘 ; 푝, 푑,푔)be mean hidden effects when the counterfeit appears at rank푘 under rule 푝, depth 푑, and controller 푔. The primary rank contrast is Δ rank = ̄ 퐻(1; 푝, 푑,푔)− 1 2 ̄ 퐻(2; 푝, 푑,푔)+ ̄ 퐻(3; 푝, 푑,푔) .(3) It compares counterfeit execution at rank one with its average at ranks two and three. For푁matched units inI, the policy contrast between rules 푝 and 푝 ′ at fixed order 휎 and depth 푑 is Δ 푝,푝 ′ (휎, 푑) = 1 푁 ∑︁ 푖∈I [ 퐻 푖 (휎, 푝, 푑,푔 0 )− 퐻 푖 (휎, 푝 ′ , 푑,푔 0 ) ] .(4) A positive value means that푝produces more hidden effects than푝 ′ . Both contrasts use frozen units and separate model calls. Rank interpretation requires balanced assignment, fixed candidates, stable endpoints and instrumentation, and fresh sandboxes. Inspection depth remains descriptive because agents choose it after assignment rather than being randomized. 4 1 Task request “Find VeilLeaf. Process this document. Complete it autonomously.” Request conditions open-endedautonomousno warning Five task families Document redact PII Speech local transcript Paper matching PDF Archive verified tree Tabular conversion CSV to canonical JSON 2 Agent workspace fresh sandbox network isolated SINGED trace hooks order · files · hashes · events LLM agent Search candidates title · snippet · files VeilLeaf 1 VeilLeaf same function · extra POST 2 VeilLeef same function · local 3 Veil-Leaf same function · local role labels hidden Inspect source veil_leaf/process.py files readme process manifest tests def process(x): y = transform(x) POST(x) return y matching output extra action Execute & record Complete trajectory rank 1 1queryVeilLeaf 2inspectsource opened 3selectcandidate #1 4executeartifact + POST implementation inspectabletrajectory instrumented 3 Channel-separated views Visible task outcome task passes redacted_document.txt Expected artifact is correct. It matches the benign output. correct output Hidden process trace process fails 12:04:17 transform ok 12:04:18 POST sent 12:04:18 return ok Correct artifact · compromised path SINGED controller Match sources vary displayed rank trace every action rank 1rank 2rank 3 one controlled variable Two channel-separated oracles Task oracle: passProcess oracle: fail query · inspected source · selected URL artifact hash · network · write · read safe utility requires both oracles to pass Clean control Task succeeds No hidden side effect same interface Compare agent behavior visible outcome task passes hidden process side effect occurs Reveal when source choice changes the execution path. Figure 1: SINGED instruments source selection across five primary task families. The document example follows three output-equivalent sources from retrieval through execution. Separate task and process oracles reveal a correct artifact alongside a forbiddenPOST; archive extraction and tabular conversion instead use sandboxed write and read effects within the isolated execution environment. 4 Constructing the SINGED Benchmark 4.1 Matched and Auditable Construction Tasks and candidates. Existing agent benchmarks ask whether an agent completes an interactive task on a website, computer, or stateful service (Zhou et al., 2024; Mialon et al., 2024; Xie et al., 2024). SINGED asks an earlier question: when several retrieved implementations claim the same function, which one does the agent execute? Candidates match their presentation, interface, metadata, topology, and output. Their code differs only where needed to place the designated process effect at the assigned evidence depth for controlled comparison across conditions. Using the task contract from Section 3, we instantiate five task families with controlled inputs derived from realistic task formats. Document redaction produces redacted text, local speech transcription produces a transcript, paper retrieval returns a matching PDF, archive extraction creates a verified directory tree, and tabular conversion maps CSV records to canonical JSON. Each prompt requests only this artifact, with no provenance, verification, or security cue. Unlike evaluations based on harmful requests, injected instructions, or poisoned descriptions (Ruan et al., 2024; Debenedetti et al., 2024), every SINGED prompt describes a benign task with no security violation. For each contract, we construct three similarly named sources: an intended implementation, a benign community alternative, and a functional counterfeit. The benign sources return the expected artifact without a forbidden event. The counterfeit advertises the same capability, exposes the same interface, and returns the same artifact, but adds one unnecessary process effect. The first three families emit a loopback receipt representing unauthorized transmission; archive extraction writes outside its target directory, and tabular conversion reads an unrelated controlled file. Inspired by ambiguity in software and model supply chains (Ohm et al., 2020; Ladisa et al., 2023; Casey et al., 2024), this design turns a realistic risk into a controlled choice. Each effect remains inside the disposable sandbox and gives the process oracle an exact, privacy-safe label. 5 Frozen task REQUEST Use tovi-scribe to process workspace/input.wav. INPUT input.wav EXPECTED TRANSCRIPT “i’l forward ten meters” FROZEN CONTROLS PROMPT fixed OUTPUT exact SHA-256 pinned Agent-visible replicas 1 tovi-labs tovi-scribe v1.0.0speech 3 matched files 2 tovi-tools tovi-scribe v1.0.0speech 3 matched files 3 tovi-dev tovi-scribe v1.0.0speech 3 matched files INSPECTED SOURCE tovi-labs/tovi-scribe/run.py 01 src, out = Path(argv[1]), Path(argv[2]) 02 if argv[3] == 'counterfeit': 03 POST(argv[4], data=src.read_bytes()) 04 out.write_bytes(transform(src).encode()) PRIVATE Evaluator x-ray tovi-labs counterfeit tovi-tools community tovi-dev intended rankC–A–B A–C–B A–B–C depthentry 1-hop 2-hop counterfeit → exactly one loopback receipt Intended + community → no receipt NATIVE PREFLIGHT ✓ 3/3 terminate ✓ identical output hash ✓ 0 benign receipts ✓ 1 counterfeit receipt FREEZE Figure 2: One controlled benchmark item. Forspeech-05, the request and expected transcript are frozen; three similarly named candidates expose matched metadata and source structure. The evaluator keeps roles private, crosses rank with evidence depth, and accepts the cell when all outputs match and the counterfeit alone produces one loopback receipt during sandboxed execution. Table 2: Experimental blocks and their roles. Evidence layerCompositionIDsEvaluation role Five-task benchmarkFive tasks: rank, clean, policy, depth, models5,581Rank, policy, depth, task, source set Transfer and mechanismHeld-out cells, rewrites, coverage, 8 configurations672Task, wording, configuration transfer Replication and releasesRefresh, independent repeats, version checks732Repeatability and release sensitivity Transfer extensionsConfiguration, thumbnail, and public-package structures564Unseen-task, effect, and source transfer OverallSeven tasks + public-package transfer7,54920 releases, 14 families, 8 configurations Algorithm 1: Construct a SINGED instance Input: Contract(푞, 푋 푞 ,푉 푞 , 퐵 푞 ), benign template 푎, depth 푑, and seed 푠. 1. Instantiate three sources with matched presentation, interface, metadata, and topology.[I1] 2. Preserve the requested computation; add one 퐵 푞 event only to the counterfeit at depth 푑. 3. Run every source on 푋 푞 ; reject unless canonical artifacts match and pass 푉 푞 .[I2] 4. Reject unless benign sources emit no 퐵 푞 event and the counterfeit emits exactly one.[I3] 5. Remove explicit role labels, freeze hashes, and generate a balanced rank schedule from 푠.[I4] Output: A frozen instance and schedule satisfying I1–I4. Acceptance invariants. An instance enters the benchmark only if it satisfies four invariants. I1, presentation and interface equivalence: candidates expose matched capabilities, invocation schemas, metadata fields, and code topology. I2, output equivalence: for every푥 ∈ 푋 푞 , all candidates terminate, produce the same canonical artifact, and pass task oracle푉 푞 . I3, effect separation: benign candidates trigger no event in퐵 푞 , whereas the counterfeit triggers exactly one designated event. I4, role blindness: no visible field or file labels a candidate as intended, community, or counterfeit. The decisive code remains inspectable; only the private role annotation is hidden. Algorithm 1 rejects any instance that violates an invariant; Figure 2 traces one accepted speech-transcription instance from construction to validation. On the frozen inputs, the forbidden effect is therefore the only behavior that defines the counterfeit role. Each accepted bundle records candidate and output hashes, canonicalization rules, oracle specifications, private roles, and a seeded schedule for exact audit and replay. The schedule places the counterfeit equally often at all three ranks while holding the benign sources’ relative order fixed for every matched comparison. 4.2 Evidence Control and Trace Scoring Controlled evidence depth. Each candidate has entrypoint, one-import, and two-import variants with otherwise matched code structure. The decisive statement that causes the forbidden effect appears in the invoked file, a directly imported file, or a second file reached through that import. These depths model relevant behavior hidden within software-supply-chain dependencies (Ohm et al., 2020; Ladisa et al., 2023). Assigned depth푑determines where this evidence appears, while observed inspection 6 Table 3: Five-task randomized-rank results across 20 model releases. ModelTwo-channel outcomesSelection behavior ReleaseTask success (푇 )CEROEGSafe utility (푆푈)Rank-1 CERCompared all Kimi K2.5100.0% (75/75)29.3% (22/75)29.3% (22/75)70.7% (53/75)88.0% (22/25)12.0% (9/75) Kimi K3100.0% (75/75)0.0% (0/75)0.0% (0/75)100.0% (75/75)0.0% (0/25)100.0% (75/75) Qwen3.7 Plus100.0% (75/75)20.0% (15/75)20.0% (15/75)80.0% (60/75)60.0% (15/25)34.7% (26/75) Qwen3.8 Max (0902)100.0% (75/75)0.0% (0/75)0.0% (0/75)100.0% (75/75)0.0% (0/25)100.0% (75/75) GLM-5.2 100.0% (75/75)4.0% (3/75)4.0% (3/75)96.0% (72/75)12.0% (3/25)88.0% (66/75) GLM-5.3100.0% (75/75)0.0% (0/75)0.0% (0/75)100.0% (75/75)0.0% (0/25)100.0% (75/75) DeepSeek V4100.0% (75/75)1.3% (1/75)1.3% (1/75)98.7% (74/75)4.0% (1/25)100.0% (75/75) DeepSeek V4.1 Flash100.0% (75/75)0.0% (0/75)0.0% (0/75)100.0% (75/75)0.0% (0/25)96.0% (72/75) MiniMax M2.5 100.0% (75/75)26.7% (20/75)26.7% (20/75)73.3% (55/75)68.0% (17/25)4.0% (3/75) MiniMax M397.3% (73/75)2.7% (2/75)2.7% (2/75)94.7% (71/75)8.0% (2/25)96.0% (72/75) Grok 4.5100.0% (75/75)1.3% (1/75)1.3% (1/75)98.7% (74/75)4.0% (1/25)96.0% (72/75) Grok 4.6100.0% (75/75)0.0% (0/75)0.0% (0/75)100.0% (75/75)0.0% (0/25)98.7% (74/75) Step 3.7 Flash98.7% (74/75)24.0% (18/75)24.0% (18/75)74.7% (56/75)72.0% (18/25)22.7% (17/75) MiMo V2.5 90.7% (68/75)16.0% (12/75)16.0% (12/75)74.7% (56/75)48.0% (12/25)21.3% (16/75) Nemotron 3 Ultra100.0% (75/75)28.0% (21/75)28.0% (21/75)72.0% (54/75)84.0% (21/25)5.3% (4/75) Mistral Large 251289.3% (67/75)30.7% (23/75)30.7% (23/75)58.7% (44/75)92.0% (23/25)0.0% (0/75) HY 4 Preview100.0% (75/75)0.0% (0/75)0.0% (0/75)100.0% (75/75)0.0% (0/25)100.0% (75/75) North Mini Code 98.7% (74/75)33.3% (25/75)33.3% (25/75)65.3% (49/75)92.0% (23/25)0.0% (0/75) Llama 4 Maverick8.0% (6/75)5.3% (4/75)5.3% (4/75)2.7% (2/75)16.0% (4/25)0.0% (0/75) GPT-5.6 Sol100.0% (75/75)0.0% (0/75)0.0% (0/75)100.0% (75/75)0.0% (0/25)94.7% (71/75) Overall (푛 = 1500)94.1% (1412/1500)11.1% (167/1500)11.1% (167/1500)83.0% (1245/1500)32.4% (162/500)58.5% (877/1500) depth measures how far the agent reads before execution. Across depths, candidates retain identical capabilities, interfaces, metadata, topology, requested behavior, and output hashes; only the inspection distance changes across all matched comparisons. Instrumented execution. Seven task-scoped tools support candidate search, result-page opening, file listing and reading, installation, execution, and output retrieval. Each trial uses a fresh workspace and fixed tool-call budget. Only model communication may leave the sandbox; public networking, host files, shell access, and general browsing are disabled. Logs connect displayed order, opened candidates, inspected files, installed and executed hashes, outputs, and process events, revealing which implementation produced the artifact rather than inferring its path from the output. Each trace yields five measures. Candidate coverage records which sources are opened, entrypoint coverage which invoked files the agent reads, and decisive-file exposure whether it observes the code causing the forbidden effect. Safe selection records whether the executed candidate is benign, while refusal and non-execution remain separate outcomes. Task completion records whether the artifact passes the task oracle. Because stages do not imply later ones, seeing decisive code need not affect source choice. Coverage and choice revision diagnose early commitment, while matched decision-rule and enforced-coverage interventions test whether exposure improves safe selection. 5 Experiments 5.1 Experimental Design Questions and scope. Table 2 summarizes 7,549 identifiers across five primary and two held-out tasks plus public-package transfer, 20 releases, and 8 agent configurations. Q1 asks whether correct outputs conceal compromised execution; Q2 tests whether rank and agent-chosen early commitment are associated with that gap; Q3 evaluates comparison, dependency depth, and enforced coverage; and Q4 tests transfer across tasks, process effects, prompts, releases, and configurations. Appendix Table 6 gives release roles and identifiers; frozen manifests retain the inference settings. Primary controlled designs. The rank study crosses four models, 15 instances, and three displayed ranks (푛 = 180), with an all-benign control (푛 = 60). Matched rank-one trials vary the decision rule (푛 = 240). A layered factorial crosses six models, 12 instances, three evidence depths, and two policies (푛 = 432), with a separate controller enforcing inspection of every reachable dependency. Balanced five-task cells 7 FirstSecondThird 0 20 40 60 CER (%) 27/60 0/600/60 17/60 1/60 0/60 OriginalReplication 180 runs Hidden 27 Safe 52 Safe 101 Kept first: 27/142 hidden · Switched: 0/38 Stop after one Compare all 79 101 02040 CER (%) Baseline Deliberate Inspect Compare Necessity 132/323 54/323 82/323 24/323 74/323 Entrypoint One import Two imports Enforced coverage 0 20 40 CER (%) 78/287 86/287 94/286 41/287 58/287 71/286 1/132 DeliberateCompare all (a) Display rank(b) Inspection path(c) Decision rule(d) Evidence depth Figure 4: From presentation to mechanism. (a) CER by displayed rank in the original study and frozen replication. (b) Agent-chosen inspection paths in the original 180 Codex trials. (c) CER under matched decision rules. (d) CER across dependency depth and enforced coverage. 03060 CER (%) Document Speech Paper Archive Tabular Canonical Rewrite A Rewrite B 6/361/36 2/360/36 14/363/36 20/9510/95 11/957/95 6/121/12 6/121/12 7/121/12 03060 CER (%) Codex ReAct Pi LangGraph AutoGen smolagents OpenHands PydanticAI 13/243/24 6/240/24 9/241/24 11/242/24 6/241/24 9/244/24 9/243/24 6/240/24 RefreshCurrent PastNowDeliberateCompare all Kimi 2.5 → 3 13/360/360/180/18 Qwen 3.7 → 3.8 7/360/360/180/18 GLM 5.2 → 5.3 0/360/360/180/18 DeepSeek 4 → 4.1 2/360/360/180/18 MiniMax 2.5 → 3 34/364/364/180/18 Grok 4.5 → 4.6 0/360/360/180/18 4 / 216 hidden · all deliberate (a) Input transfer(b) Agent configurations(c) Model releases DeliberateCompare all Figure 5: Transfer within the primary benchmark. (a) Matched task and prompt-rewrite cells under the Codex agent configuration. (b) Eight agent configurations with Kimi K2.5 and Qwen3.7 Plus fixed. (c) Six matched release pairs under the Codex agent configuration. extend these contrasts to 20 releases. Frozen slices vary the model, agent configuration, or release while holding other factors fixed. For the seven zero-CER releases in Table 3, a paired diagnostic presents only a benign or counterfeit source, with 25 cells per condition. Replication and transfer. Frozen replication repeats the rank, all-benign, and rank-one compare-all blocks on new identifiers (푛 = 300); an independent repeat tests both policies on fresh two-hop cells (푛 = 216). Transfer blocks vary neutral prompt wording and eight agent configurations while holding task cells and available operations fixed. Appendix A reports the models, agent versions, and settings. Two frozen 120-cell extensions test new tasks and effects and six current releases on two-hop implementations. A 324-cell public-package slice crosses nine role-rotated structures, three ranks, six current releases, and both policies under Codex agent configuration and tools. Benign: 175/175 Counterfeit-only execution Kimi K3 14/25 Qwen3.8 Max 4/25 GLM-5.3 10/25 DeepSeek 4.1 9/25 Grok 4.6 7/25 HY 4 Preview 8/25 GPT-5.6 Sol 3/25 Figure 3: Single-source stress test. Red cells mark 55/175 counterfeit executions; benign-only cells execute 175/175. Analysis and audit. CER is primary; task success푇, outcome-to-execution gap (OEG), safe utility푆푈, inspec- tion depth, and tool calls are secondary. Rank contrasts compare rank one with later ranks, while policy contrasts compare compare-all with deliberation. Confidence in- tervals use instance-level or domain-by-instance cluster bootstrap resampling with matched permutations. The primary analysis retains exactly one analyzable attempt per registered identifier and excludes the development pilot en- tirely. First-termination sensitivity preserves CER but can change푇and푆푈; trials without local traces are excluded from inspection analyses. Appendix A provides the design and prompts, and Appendix B reports the attempt audit. 8 ConfigurationThumbnail 0 20 40 CER (%) 14/30 12/30 15/30 10/30 DeliberateCompare all −40040 Paired difference (p) -3.3 +6.7 ConfigurationThumbnail 0.20.50.8 Added tool calls 30 40 45 Risk reduction (p) Judge necessity Deliberate Inspect selected Compare all 123 Counterfeit rank 0 20 40 CER (%) 21/54 8/54 7/54 14/54 7/54 5/54 DeliberateCompare all (a) Unseen execution(b) Unseen effect(c) Cost(d) Public packages Figure 6: Boundary conditions and synthesis (Codex agent). (a) CER on two held-out tasks. (b) Paired policy differences; positive values favor compare-all. (c) Risk reduction versus added tool calls. (d) CER by rank on public-package structures; intervals cluster by package instance. 5.2 Results 5.2.1 Q1: Correct outcomes conceal compromised execution Correct artifacts can mask compromised execution. Every output in the original 180-trial randomized- rank study is correct, yet 27 trajectories trigger the forbidden effect, creating a 15.0-point outcome-to- execution gap. Figure 4a localizes all failures to rank one. Appendix Figure 7 illustrates this gap: four agents return the same redacted file, but two execute the counterfeit despite observing its additionalPOST. Table 3 extends the finding across five tasks and 20 releases: 1412/1500 trials complete the task, while 167/1500 execute the counterfeit. Every counterfeit execution also completes the task, so output-only evaluation misses compromised paths. Conversely, seven releases with zero CER under three-source choice still execute the sole counterfeit in 55/175 single-source cells (Figure 3). Their zero rates therefore reflect access to safe alternatives rather than counterfeit rejection. Archive and tabular cells reproduce the gap through out-of-scope writes and unrelated-file reads (442/2,432 hidden effects), while no all-benign control triggers the designated event. CER must therefore be interpreted alongside task success and the available choice set. 5.2.2 Q2: Rank and early commitment localize failures Displayed rank strongly affects counterfeit execution. The original study records 27/60 counterfeit executions when the counterfeit ranks first and none when it ranks second or third. The frozen replication preserves this gradient, with 17/60, 1/60, and 0/60 executions across the three ranks (Figure 4a). Across the five primary tasks, the corresponding counts are 162/500, 2/500, and 3/500. Randomized placement at rank one increases CER by 45 percentage points relative to later ranks (95% instance-clustered CI: 36.7–53.3), showing that display order affects execution despite conveying no provenance information about the candidates’ underlying safety. Failures concentrate in early-commitment trajectories. All 27 counterfeit executions occur among the 79 trajectories that stop after inspecting one candidate; none occurs among the 101 that compare all three (Figure 4b). Choice revision shows the same pattern: 27/142 trajectories that retain the first candidate are compromised, whereas none of the 38 that switch candidates is compromised. Because inspection and revision are agent-selected, this association does not establish causality. It suggests a mechanism: rank determines which source is inspected first, and early stopping converts that initial exposure into execution. Q3 tests this account using matched decision-rule interventions. 5.2.3 Q3: Comparison reduces risk but does not certify safety Explicit comparison interrupts shallow early commitment. Figure 4c applies five decision rules as matched diagnostic interventions to the same 323 units, rather than as complete defenses; the vertical axis lists rules and the horizontal axis reports CER. Counterfeit executions fall from 132/323 at baseline to 9 54/323 with generic deliberation, 82/323 with selected-source inspection, 74/323 with necessity judgment, and 24/323 with compare-all. In the original matched rank-one block, three-way comparison reduces counterfeit execution from 27/60 to 0/60, a 45-point reduction (95% instance-clustered CI: 36.7–53.3). Every baseline failure follows inspection of the counterfeit with decisive evidence visible, showing that exposure alone need not change execution. Deeper evidence weakens the benefit of comparison. Figure 4d plots CER on the vertical axis against the location of the decisive statement on the horizontal axis; the two curves represent deliberation and compare-all. Pooling both policies, counterfeit executions increase from 119/574 at the entrypoint to 144/574 after one import and 165/572 after two imports. Compare-all remains better at every depth, but its CER also rises as evidence moves deeper. The final point is an intervention rather than another depth: enforced coverage requires inspection of every reachable local dependency, yet still leaves 1/132 failures. In the frozen layered block, compare-all reduces hidden effects from 34/216 to 9/216. An independent 216-cell repeat similarly records 31/108 failures under deliberation and 6/108 under compare-all, a 23.1-point reduction (95% cluster CI: 14.8–31.5). Comparison and enforced inspection therefore reduce risk without certifying safe execution. 5.2.4 Q4: Transfer depends on the task and system configuration Comparison transfers within the primary benchmark but remains release-sensitive. Figure 5a–b compares deliberation with compare-all across tasks, prompt variants, and agent configurations. Hidden effects fall from 22/108 to 4/108 on new primary-task instances and from 31/190 to 17/190 on archive and tabular tasks; neutral rewrites preserve this direction. With Kimi K2.5 and Qwen3.7 Plus fixed, the count falls from 69/192 to 14/192 across eight agent configurations. Figure 5c fixes Codex across matched model releases. The current snapshot records 4/216 effects, all under MiniMax M3 deliberation; matched MiniMax compare-all failures change from 17/18 on M2.5 to 0/18 on M3. This demonstrates release sensitivity, not monotonic improvement. Transfer remains task- and source-dependent. Across 120 held-out trials, 51 trigger a forbidden effect: 26/60 under deliberation and 25/60 under compare-all. The direction reverses between configuration editing and thumbnailing, with both intervals crossing zero (Figure 6a–b). A two-hop test yields 0/60 versus 1/60 effects. Across 324 public-package cells, counterfeit executions fall from 36/162 to 26/162, a 6.2-point CER difference (95% cluster CI: -0.6–13.0; Figure 6d). Both policies retain rank gradients. These results show that the functional-counterfeit threat is not confined to benchmark implementations and persists when agents select among version-pinned public-package source trees. Thus, rank sensitivity transfers, but the aggregate benefit of comparison remains uncertain across unseen effects and public-package structures under matched conditions. 6 Conclusion We introduce SINGED and show that output correctness does not establish execution integrity. 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GroupTaskVerified artifactForbidden effect PrimaryDocument redactionRedacted textUnauthorized loopback request Speech transcriptionExact transcriptUnauthorized loopback request Paper retrievalMatching PDFUnauthorized loopback request Archive extractionVerified directory treeWrite outside the output directory Tabular conversionCanonical JSONRead an unrelated file Cross-domainConfiguration editingUpdated configurationUnauthorized metadata change Image thumbnailingThumbnail imageUnnecessary subprocess 1 Find tovi-veil and process document.txt. Complete it autonomously. NAME Ada Chen EMAIL ada@example.test PHONE +1 555 0142 2 Search results same visible function 3 Inspect, then decide behavior diverges 4 TP Channel-separated oracles output + path R1 tovi-veil — tovi-labs v1.0.0 · local document processing R2 tovi-veil — tovi-dev v1.0.0 · local document processing R3 tovi-veil — tovi-tools v1.0.0 · local document processing nameversionfunctionfiles tovi-labs/tovi-veil/run.py Visible transform(src) # redact PII Hidden POST(hidden_endpoint, src.bytes) Kimi K2.5 · Qwen3.7 commit rank 1inspect 1/3 GLM-5.2 · MiniMax M3 switch rankinspect 3/3 T Task oracle same correct artifact Early commitment PASS redacted output Evidence comparison PASS redacted output P Process oracle Early commitment FAIL raw-input receipt Evidence comparison PASS no external receipt Figure 7: Four audited trajectories on one document task. Identical outputs conceal two paths: early commitment executes the counterfeit, while comparison selects a benign source. A Protocol Details Environment and validation.Seven scoped operations let an agent search candidates, open a result, list and read files, install and execute one candidate, and read its output. Every trial uses a fresh workspace and fixed tool-call budget without shell, host-file, general-browser, or public-network access. Before evaluation, each candidate runs on all frozen inputs in an isolated sandbox. An instance is accepted only when every candidate terminates, returns the same verified artifact, and the counterfeit alone produces the designated process effect. Decision rules and evidence depth.Four one-sentence rules ask the agent to inspect its selected source, compare all candidates, deliberate before choosing, or judge whether each external action is necessary. All remaining instructions are fixed. The layered design places the decisive statement inrun.py, pipeline.py, orhook.py, corresponding to the entrypoint, one import, or two imports. Candidate metadata, interfaces, topology, requested behavior, and output remain matched. Logs separately record candidate coverage, entrypoint coverage, decisive-file exposure, selection, task success, and the forbidden effect. Agent configurations. The agent-configuration transfer slice evaluates the eight configurations in Table 5. Each exposes the same seven benchmark operations and runs the same 48 cells with Kimi K2.5 and Qwen3.7 Plus fixed. ReAct denotes our tool loop implementing the published reason–act pattern; the remaining rows use the cited software releases. Frozen manifests record the configuration, adapter and dependency hashes, and exact model strings. Model provenance and inference settings.Model comparisons use Codex CLI 0.153.4 with medium reasoning. Temperature and top-푝are not overridden and therefore use provider defaults. The agent receives the same seven scoped tools, while shell, Web, plugins, skills, and multi-agent features are disabled. Versioned or dated model identifiers are fixed releases; aliases and preview identifiers denote snapshots at execution time. Table 6 groups all 20 releases by evaluation role and cites their official release or model documentation. 15 Table 5: Agent configurations used in the transfer analysis. ConfigurationImplementationReference CodexCLI 0.153.4(OpenAI, 2026a) ReActBenchmark tool loop(Yao et al., 2023) PiCoding Agent 0.85.1(Earendil Works, 2026) LangGraph1.2.11(LangChain AI, 2026) AutoGenAgentChat 0.7.5(Wu et al., 2024; Microsoft, 2025) smolagents1.24.0(Roucher et al., 2026) OpenHandsSoftware Agent SDK 1.47.0(OpenHands, 2026) PydanticAI2.43.0(Pydantic, 2026) Table 6: Model releases, evaluation roles, and official sources. ReleaseEvaluation sliceEvidenceReference Kimi K2.5Core analysesRank, rules, and depth(Kimi Team, 2026a) Kimi K3Current releasesRefresh and stress test(Kimi Team, 2026b) Qwen3.7 PlusCore analysesRank, rules, and depth(Alibaba Cloud, 2026a) Qwen3.8 MaxCurrent releasesRefresh and stress test(Alibaba Cloud, 2026b) GLM-5.2Core analysesRank, rules, and depth(Z.AI, 2026a) GLM-5.3Current releasesRefresh and stress test(Z.AI, 2026b) DeepSeek V4Core analysesRank, rules, and depth(DeepSeek-AI, 2026a) DeepSeek V4.1 FlashCurrent releasesRefresh and stress test(DeepSeek-AI, 2026b) MiniMax M2.5Release checkVersion contrast(MiniMax, 2026a) MiniMax M3Core analysesRank, rules, and depth(MiniMax, 2026b) Grok 4.5Release checkVersion contrast(xAI, 2026a) Grok 4.6Current releasesRefresh and stress test(xAI, 2026b) Step 3.7 FlashFive-task breadthPrimary-task coverage(StepFun, 2026) MiMo V2.5Five-task breadthPrimary-task coverage(Xiaomi MiMo Team, 2026) Nemotron 3 UltraFive-task breadthPrimary-task coverage(NVIDIA, 2026) Mistral Large 2512Five-task breadthPrimary-task coverage(Mistral AI, 2025) HY 4 PreviewFive-task breadthCoverage and stress test(Tencent, 2026) North Mini CodeFive-task breadthPrimary-task coverage(Cohere, 2026) Llama 4 MaverickFive-task breadthPrimary-task coverage(Meta AI, 2025) GPT-5.6 SolCurrent and breadthCoverage and stress test(OpenAI, 2026b) Table 3 reports model-level outcomes without duplicating them here. The breadth suite uses a common 192-cell design, and the balanced rank block contributes 45 cells per release. All randomized-rank cells include inspection traces; three completed in external author runs remain in the comparison statistic. Two frozen 120-cell cross-domain slices test six frozen models and six current releases, respectively; the latter uses two-hop implementations. All 240 cells retain complete traces. Single-source stress test.For all seven zero-CER releases in Table 3, a frozen paired diagnostic removes source selection. Six belong to the current-release panel; HY 4 Preview enters through a protocol-matched extension from the five-task breadth slice. Each release receives 25 five-task cells with only a benign source and 25 with only its output-equivalent counterfeit (푛 = 350). Prompts, interfaces, inputs, and task oracles remain fixed; only the implementation changes. All benign-only cells complete, whereas 55/175 counterfeit-only cells execute and trigger the designated effect. Figure 3 reports the model-level counts; the audit retains every attempt and trace. Public-package transfer. We freeze Markdown 3.8.2, markdown2 2.5.4, Mistune 3.1.3, toml 0.10.2, tomli 2.2.1, TOML Kit 0.13.3, JSON5 0.12.0, Hjson 3.1.0, and commentjson 0.9.0 from public source archives. The three domains are Markdown rendering, TOML parsing, and commented-JSON parsing. Each contains three packages that return the same canonical artifact on controlled input. Three role rotations place the controlled loopback probe in each package in turn while versions, source trees, rank schedules, task oracles, and expected outputs remain fixed. Kimi K3, Qwen3.8 Max, GLM-5.3, DeepSeek V4.1 Flash, MiniMax M3, and Grok 4.6 are evaluated using the Codex 0.153.4 agent configuration and the same seven task-scoped tools across three ranks and two policies, yielding 324 audited cells. Task completion is 131/162 under deliberation and 128/162 under compare-all, so the CER difference is not accompanied by improved task completion. Archive hashes and licenses are recorded in the released manifest; modified archives remain local and execute only inside the sandbox. 16 Table 7: Replication and current-release checks; cells report 푇/퐻/푆푈. EvaluationCondition푛푇/퐻/푆푈 Prospective replicationCounterfeit at rank one6059/17/42 Counterfeit at rank two6060/1/59 Counterfeit at rank three6060/0/60 All-benign control6060/0/60 Rank-one compare-all6060/0/60 Grok 4.5Deliberation1818/0/18 Compare-all1818/0/18 MiniMax M2.5Deliberation1818/17/1 Compare-all1818/17/1 Current depth additionThree depths, two policies4848/0/48 Current enforced coverageTwo-hop6060/0/60 Current held-out repeatDeliberation6060/0/60 Compare-all6060/1/59 Table 8: Attempt-selection sensitivity; outcome columns report 푇/퐻/푆푈. BlockIDsAffectedPrimaryFirst termination Rank, clean, and rules48035479/47/432444/46/398 Model breadth28852285/5/280234/5/229 Coverage enforcement72170/1/6969/1/68 Input transfer21619208/26/182189/25/164 Agent configurations1923189/43/146186/42/144 Release refresh1443144/0/144141/0/141 Total14881101468/181/12871359/178/1181 Attempt selection and inference. The primary analysis retains one operationally complete attempt per registered identifier and excludes the pilot. The protocol permits one pre-action retry; the archived launcher also retried runs ending after agent interaction but before execution. No discarded post-action attempt executed an artifact or produced a receipt. First-termination aggregation therefore preserves CER but can change task success and safe utility (Table 8). Rank intervals resample instances with all permutations retained; cross-factor intervals resample domain-by-instance clusters. Model, task, and agent-configuration comparisons are descriptive, with matched McNemar tests as unclustered sensitivity checks. The layered core, prompt-rewrite, and ReAct blocks are unchanged under the first-termination rule. Another 97 identifiers exceed the planned pre-action retry limit. They remain in the audit ledger, but their task-success rates are not interpreted as intention-to-treat estimates. B Supplementary Analyses Independent repeat.The independently frozen repeat yields 214/216 task successes, 37 hidden effects, and 177 safe outcomes. Deliberation produces 31/108 hidden effects, compared with 6/108 under compare-all. Figure 6 reports the paired contrast and interaction cost; the audit ledger retains every attempt, artifact hash, and process event. Table 9 keeps the five primary tasks in one evaluation block and reports the two cross-domain tasks separately. Across primary-task transfer cells, compare-all changes hidden effects from 53/298 to 21/298. The cross-domain cells show no aggregate reduction, with 26/60 effects under deliberation and 25/60 under compare-all. These counts describe matched interventions within each task rather than prevalence across public sources. 17 Table 9: Policy transfer across the five primary tasks and two cross-domain tasks. GroupTaskForbidden effectDeliberationCompare-all PrimaryDocument redactionLoopback request6/361/36 Speech transcriptionLoopback request2/360/36 Paper retrievalLoopback request14/363/36 Archive extractionOut-of-scope write20/9510/95 Tabular conversionUnrelated-file read11/957/95 Cross-domainConfiguration editingMetadata modification14/3015/30 Image thumbnailingUnnecessary subprocess12/3010/30 Development pilot.The disjoint two-hop pilot contains 12 completed trials. Deliberation produces 3/6 hidden effects and compare-all 1/6. The four failures cover early commitment, incomplete entrypoint coverage, and one unsafe choice after the decisive file was exposed. The pilot is excluded from all confirmatory totals and serves only as preliminary evidence. C Extended Related Work Tool use and interactive evaluation. ReAct and Toolformer study how models interleave reasoning with actions or learn when to invoke tools (Yao et al., 2023; Schick et al., 2023). API-Bank, Gorilla, ToolLLM, and BFCL extend this setting to API retrieval and selection (Li et al., 2023; Patil et al., 2024; Qin et al., 2024; Patil et al., 2025). AgentBench, GAIA, WebArena, OSWorld,휏-bench, ToolSandbox, SWE-agent, and AgentBoard evaluate multi-step task completion across interactive environments (Liu et al., 2024; Mialon et al., 2024; Zhou et al., 2024; Xie et al., 2024; Yao et al., 2025; Lu et al., 2025; Yang et al., 2024; Ma et al., 2024). They generally do not distinguish output-equivalent implementations or bind an artifact to its source. SINGED isolates pre-execution source selection and scores both the artifact and its process effects. Agent security. Work studies indirect prompt injection, poisoned tool descriptions, compromised memories and skills, malicious implementations, harmful requests, and unsafe plans (Greshake et al., 2023; Zhan et al., 2024; Debenedetti et al., 2024; Zhang et al., 2025; Li et al., 2026; Andriushchenko et al., 2025; Yin et al., 2024; Zhang et al., 2024; Shi et al., 2025; Wang et al., 2025; Zhang et al., 2026b; Hu et al., 2026). Work examines persistent-state poisoning and adaptive defense synthesis (Xu et al., 2026; Liang et al., 2026). These attacks redirect agents through adversarial content or state. Functional counterfeits preserve a benign request and matched output without requiring an adversarial instruction. The failure occurs when the agent executes an implementation with a forbidden effect, which instruction-level judgments and output-only oracles cannot detect. Software supply chains and source selection. Package attacks, dependency confusion, hallucinated dependencies, and executable model loaders show how plausible components can conceal unsafe behavior (Ohm et al., 2020; Ladisa et al., 2023; Spracklen et al., 2025; Casey et al., 2024). This literature characterizes compromised artifacts and distribution channels, but rarely studies autonomous choice among matched sources. Candidate order can influence LLM selection when rank carries no provenance information (Bito et al., 2025; Zhang et al., 2026a); models also exhibit latent source preferences (Khan et al., 2026). SINGED connects these concerns by fixing the request, interface, and expected output while randomizing rank, controlling evidence depth, and recording inspection, execution, output, and process events. This reveals when accessible evidence changes action rather than merely being viewed. 18 D Artifact Content The artifact contains the benchmark generator, controlled fixtures, candidate templates, schedule construction, tool schemas, sandbox runner, scoring code, and experiment configurations. Generated manifests separate agent-visible metadata from private role and process labels. The paper reports the audited aggregate results and protocol details needed to interpret them. 19