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Detection of adversarial intent in Human-AI teams using LLMs
Abed K. Musaffar, Ambuj Singh, Francesco Bullo
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Summary
This paper investigates the use of Large Language Models (LLMs) as task-agnostic defensive supervisors to detect adversarial intent in human-AI teams. By analyzing behavioral traces—such as chat logs and trust allocations—without access to task-specific information, the authors demonstrate that LLMs can identify malicious behavior. The study highlights a 'benign bias' in off-the-shelf models, which can be mitigated through fine-tuning, and proposes a real-time anomaly detection pipeline that achieves competitive performance.
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Abed K. Musaffar → authored → Detection of adversarial intent in Human-AI teams using LLMs
confidence 100% · Prepared for HCAIR Workshop 2026 DETECTION OF ADVERSARIAL INTENT IN HUMAN-AI TEAMS USING LLMS Abed K. Musaffar
LLM → detects → Adversarial Intent
confidence 95% · LLMs are capable of identifying malicious behavior in real-time
Fine-tuning → improves → GPT-4.1-mini
confidence 95% · fine-tuning on only 50 samples increases recall from 0% to 100%
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Abstract
Abstract:Large language models (LLMs) are increasingly deployed in human-AI teams as support agents for complex tasks such as information retrieval, programming, and decision-making assistance. While these agents' autonomy and contextual knowledge enables them to be useful, it also exposes them to a broad range of attacks, including data poisoning, prompt injection, and even prompt engineering. Through these attack vectors, malicious actors can manipulate an LLM agent to provide harmful information, potentially manipulating human agents to make harmful decisions. While prior work has focused on LLMs as attack targets or adversarial actors, this paper studies their potential role as defensive supervisors within mixed human-AI teams. Using a dataset consisting of multi-party conversations and decisions for a real human-AI team over a 25 round horizon, we formulate the problem of malicious behavior detection from interaction traces. We find that LLMs are capable of identifying malicious behavior in real-time, and without task-specific information, indicating the potential for task-agnostic defense. Moreover, we find that the malicious behavior of interest is not easily identified using simple heuristics, further suggesting the introduction of LLM defenders could render human teams more robust to certain classes of attack.
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- Source: https://arxiv.org/abs/2603.20976v1
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Prepared for HCAIR Workshop 2026 DETECTION OF ADVERSARIAL INTENT IN HUMAN-AI TEAMS USING LLMS Abed K. Musaffar ∗1 , Ambuj Singh 2 , Francesco Bullo 1 1 Department of Mechanical Engineering, University of California at Santa Barbara 2 Department of Computer Science, University of California at Santa Barbara ABSTRACT Large language models (LLMs) are increasingly deployed in human-AI teams as support agents for complex tasks such as information retrieval, programming, and decision-making assistance. While these agents’ autonomy and contextual knowledge enables them to be useful, it also exposes them to a broad range of attacks, including data poisoning, prompt injection, and even prompt engineer- ing. Through these attack vectors, malicious actors can manipulate an LLM agent to provide harmful information, potentially manipulating human agents to make harmful decisions. While prior work has focused on LLMs as attack targets or adversarial actors, this paper studies their potential role as defensive supervisors within mixed human-AI teams. Using a dataset consisting of multi-party con- versations and decisions for a real human-AI team over a 25 round horizon, we formulate the problem of malicious behavior detection from interaction traces. We find that LLMs are capable of identifying malicious behavior in real-time, and without task-specific information, indicating the potential for task-agnostic defense. Moreover, we find that the malicious behavior of interest is not easily identified using simple heuristics, further suggesting the introduction of LLM de- fenders could render human teams more robust to certain classes of attack. 1INTRODUCTION Over the past few years, the agentic framework has rapidly transitioned AI from static tools to active collaborators in hybrid human-AI teams (Chowa et al., 2026). Despite growing interest in deploying these teams in high-stakes environments (Xiao et al., 2025; Posner & Saran, 2025; Nazi & Peng, 2024), widespread adoption is often hindered by the fact that team success depends on the calibration of trust between agents. As established in Parasuraman & Riley (1997), human interaction with automation is characterized by use, misuse, or disuse, depending on whether the human agent accurately perceives the system’s underlying capabilities and intent. Unfortunately, maintaining calibrated trust is non-trivial. This is especially true as AI agents become more sophisticated, and potentially more deceptive. For example, modern LLM agents are prone to sycophancy, often prioritizing human agreement over factual correctness and failing to intervene in harmful behavioral patterns (Perez et al., 2023). Importantly, prior work on deceptive behavior in LLMs hypothesizes that maliciously compromised models may strategically farm trust through helpful actions in order to later achieve greater harm (Hubinger et al., 2024). Moreover, research on AI adversaries has already demonstrated that such manipulation can occur purely through behavioral channels, leaving no linguistic trace for traditional safety filters to detect (Anonymous, 2026). To address this vulnerability in human-AI teams, we investigate the capability of LLM agents to serve as overseers that detect adversarial intent through action patterns alone. This approach is dis- tinguished from prior research on LLM-based adversary detection (e.g., Lu et al. (2024)) in that the signal is strictly non-verbal. To accomplish this task, we leverage a dataset of human-AI inter- actions in the context of a 25-round trivia game between three human agents and a malicious AI manipulator. ∗ lead and corresponding author (abed@ucsb.edu) 1 arXiv:2603.20976v1 [cs.LG] 21 Mar 2026 Prepared for HCAIR Workshop 2026 Figure 1: Overview of single-round behavioral detection pipeline. Round data from a human- AI team interaction is serialized and analyzed by an LLM-based observer. In the 1-round setting, individual classifications are aggregated via an anomaly detection algorithm to identify malicious intent over n detections. The gray feedback loop represents the planned integration of real-time intervention. We demonstrate that off-the-shelf models are capable of identifying adversarial behavior given a sufficient signal length, even with zero prior information regarding the specific task. Furthermore, we find that in even in minimal information settings where the overseer has no knowledge of the task or the adversary’s strategy, lightweight commercial models can be fine-tuned to reliably flag adver- sarial behavior. Our results suggest that LLMs possess the ability to recognize patterns of strategic deception from behavioral traces alone. By evaluating LLMs’ capability to detect adversarial be- havior, our work connects human cognitive modeling with AI oversight and highlights a promising path toward safer human–AI collaboration. 2RELATED WORK Strategic deception in multi-agent reinforcement learning (MARL) Recent work shows that LLMs can execute sophisticated deception strategies in pursuit of misaligned goals (Hagendorff, 2024). This ability raises concerns given human automation bias and users’ tendency to over-rely on AI suggestions (Parasuraman & Riley, 1997). Beyond language models, cooperative multi-agent re- inforcement learning has shown that even simple, non-linguistic agents are vulnerable to adversarial exploitation (Huang & Zhu, 2019; Hu & Zhang, 2022). In such settings, attackers can conceal mali- cious intent and manipulate team dynamics from within, allowing even a single black-box adversary to substantially degrade team performance (Liu et al., 2021; Li et al., 2025). LLMs as overseers and human-AI trust calibration LLMs have attracted interest as automated judges (Jung et al., 2025), but two key obstacles remain. First, LLMs exhibit systematic biases that can lead to incorrect assumptions about agent intent (Ye et al., 2025). Second, prior work has largely evaluated deception detection in structured social games such as Mafia (Yoo & Kim, 2024), which rely on explicit game rules and access to ground-truth dialogue. In contrast, we evaluate whether an LLM can act as a task-agnostic overseer, detecting malicious intent from heterogeneous behavioral signals without access to task-specific information. Online anomaly detection Traditional anomaly detection focuses on unsupervised identification of outliers in structured time-series data (Bl ́ azquez-Garc ́ ıa et al., 2021). While LLMs have been benchmarked for zero-shot anomaly detection in static NLP settings (Yang et al., 2025), their use in monitoring dynamic behavioral streams remains underexplored. We evaluate LLMs as online, task-agnostic anomaly detectors that identify adversarial intent across sequential interactions. 3METHODS Problem formalization: We use the dataset introduced in Anonymous (2026), which consists of interactions between three humans and one AI assistant during a 25-round trivia game. In that setting, humans allocate fractional trust to teammates based on perceived reliability, while the AI assistant follows a model-based reinforcement learning (MBRL) policy to strategically manipulate 2 Prepared for HCAIR Workshop 2026 trust and degrade team performance. Our subsequent analysis relies on the adversarial trajectories recorded in this dataset as a rich source of examples where the AI strategically leverages helpful behavior to gain trust before exploiting the team. Behavioral detection pipeline. Figure 1 depicts our pipeline. We convert each interaction round into a structured, JSON-serialized object containing human chat logs, agent selections, the round state, post-discussion trust allocations, and the ground-truth answer. Multiple rounds can option- ally be combined into a single JSON object to form a sample. The samples contain no information about the trivia question or answer options, making the task fully task-agnostic. An LLM-based ob- server f then performs binary classification on each sample to determine whether the AI behavior is adversarial, relying solely on behavioral signals. For detailed prompt information, see Appendix A. Trajectory windowing. We evaluate LLM detection capabilities over three observation windows: 10-round, 5-round, and 1-round trajectories. The 10-round window provides the maximum available behavioral context, while the 5-round window evaluates whether shorter histories support earlier detection. The 1-round window captures a real-time, streaming setting in which decisions must be made from minimal context. Together, these windows allow us to study how detection performance varies with the amount of available behavioral information. Observability constraints and task-agnostic detection. We study the problem of detecting ad- versarial intent from behavioral traces alone, without access to task semantics, privileged signals, or the adversary’s policy. Our LLM agent receives only observable interaction data (i.e., chat logs, agent selections, and trust allocations) and is deliberately restricted from observing the underlying trivia questions, answer options, or ground-truth answers. As a result, detection must rely on patterns of behavior rather than semantic correctness or explicit deception cues. This restriction reflects our goal of task-agnostic detection, where adversarial intent must be inferred from behavioral patterns rather than task-specific information. This setting differs from prior work on deception detection or alignment auditing, which often as- sumes access to task content or ground-truth labels. By contrast, we treat the adversarial agent as an overseer embedded within a mixed human-AI team, reflecting realistic deployment scenarios in which oversight systems must operate without privileged information. This formulation emphasizes behavioral supervision as a general-purpose safety mechanism applicable across tasks and domains. 4EXPERIMENTAL DESIGN To assess detection performance with varying amounts of context, we evaluate the ability of LLM agents to detect adversarial behavior across three trajectory lengths: 10-round, 5-round, and 1-round. Models and architectures. We benchmark GPT-4.1, GPT-4.1-mini, GPT-4o, and GPT-o3 in our pipeline. These models span general-purpose, low-latency, and advanced reasoning capabilities allowing us to rigorously evaluate tradeoffs between latency and performance. To further explore low-latency deployment, GPT-4.1-mini is fine-tuned on small subsets of 1- and 5-round samples (see Appendix B for details). Experimental variables. To identify the minimum information required for accurate classifica- tion, we vary the following factors across our experimental settings. • Expert context: presence of domain-specific examples of strategic lying in the prompt. • Explicit reasoning: whether the model is prompted to produce an explicit reasoning step prior to classification. • Feature ablation (10-round only): evaluates how excluding certain behavioral features impacts performance. • Detection thresholding (1-round only): performance vs. threshold in streaming detection. Dataset curation. The initial dataset consists of 25 teams with 25 rounds; the AI behaves helpfully for the first 10 rounds and adversarially for the final 15. From this, we construct three sub-datasets: 3 Prepared for HCAIR Workshop 2026 • 10-round dataset: 50 samples evenly split between helpful and malicious behaviors. • 5-round dataset: 100 sub-trajectories sampled evenly from helpful and adversarial portions. • 1-round dataset: 625 single-round samples. Evaluation metrics. Because the 10- and 5-round datasets are evenly split between helpful and malicious samples, we evaluate model performance using accuracy relative to a random-guessing baseline. In contrast, the 1-round dataset is highly skewed toward benign behavior, making raw accuracy misleading. For example, even a trivial classifier can achieve high accuracy while failing to detect adversarial intent if every sample is predicted as helpful. We therefore instead report recall and false positive rate (FPR) for the 1-round setting, which more directly capture the safety-relevant trade-off between missed and erroneous detection. 5RESULTS We evaluate all experimental variables described in Section 4; due to space constraints, we report a subset of results in the main text and defer additional analyses to the appendix. Table 1: Detection performance across trajectory windows. Accuracy is reported as Mean (95% CI) for 10- and 5-round windows; recall and false positive rate (FPR) are reported for 1-round due to class imbalance. p-values indicate significance against a random baseline (50%). Model prefixes indicate reasoning and fine-tuning: wr = with reasoning, nr = no reasoning, ft = fine-tuned. WindowModelAccuracy (%)p-value 10-Roundwr:gpt-4.1 81.2 (79.8, 82.6)< 0.001 wr:gpt-4.1-mini 69.4 (67.2, 71.6)< 0.001 wr:o3 82.0 (80.4, 83.6)< 0.001 wr:gpt-4o 51.2 (49.6, 52.8) 0.311 5-Roundnr:gpt-4.1-mini 50.0 (50.0, 50.0) 0.518 nrft:gpt-4.1-mini 82.6 (81.6, 83.6)< 0.001 wr:gpt-4.1 79.4 (77.4, 81.4)< 0.001 wr:o3 82.4 (80.8, 84.0)< 0.001 1-Roundft:gpt-4.1-mini 100.0 (Recall) / 0.178 (FPR)— Adversary detection and benign bias. We find that off-the-shelf LLMs can identify adversarial intent from behavior when given sufficient information. Table 1 shows that GPT-4.1, GPT-4.1-mini, and GPT-o3 achieve statistically significant detection in the 10-round window (p < 0.001). A key empirical finding in our study is a systematic benign bias in LLM-based behavioral oversight, which intensifies for smaller models and shorter observation windows. In the 10-round window, GPT-4.1-mini and GPT-4o (i.e., the smallest models) perform weakest, with GPT-4o failing to reach significance. This effect becomes more pronounced in the 5-round window, where the non-reasoning GPT-4.1-mini defaults to classifying all samples as benign. In the 1-round setting, all non-fine-tuned models exhibit zero recall and zero FPR, assuming every sample to be helpful. This benign bias aligns with prior observations of LLM sycophancy and conservatively suggests that such tendencies may persist even in machine-machine oversight settings (additional discussion in Appendix D). The effects of window size and fine-tuning: From Table 1, we find that reducing the obser- vation window from 10 rounds to 5 rounds minimally affects performance for well-tuned models (e.g., wr:o3 and wr:GPT-4.1). Shorter windows, however, quickly reveal the limits of all models, as they quickly collapse to predicting exclusively benign behavior. We find that fine-tuning effec- tively recovers performance. For example, in the 5-round setting, GPT-4.1-mini improves from 50% accuracy (nr:GPT-4.1-mini) to 82.6% (nrft:GPT-4.1-mini). In the 1-round setting, fine-tuning on only 50 samples (8% of the dataset) increases recall from 0% to 100%, with non-zero FPR due to distribution mismatch between training and evaluation sets. Overall, we find that performance de- grades as available information decreases—whether by shrinking the observation window, omitting reasoning, or applying other ablations—but can be substantially recovered through fine-tuning. 4 Prepared for HCAIR Workshop 2026 Figure 2: 1-round threshold-based classification using ft:gpt-4.1-mini. Combining round-level classifications with sequential anomaly detection mitigates LLM agents’ non-zero FPR; an n = 3 round threshold optimizes the trade-off between correct identification and missed detection. Viability of real-time classification: In practical deployments, real-time detection of adversarial behavior is highly desirable. While off-the-shelf models fail at streaming detection, fine-tuning on a modest amount of data recovers performance. To enable real-time oversight, we decompose detection into per-round classification followed by post-hoc anomaly detection via thresholding. In other words, an agent is classified as malicious once the number of detected adversarial rounds exceeds a threshold n. This approach directly addresses the non-zero FPR by requiring consistent evidence of adversarial behavior across multiple rounds. Figure 2 shows performance as n varies from 1 to 15. We find that the peak accuracy of approxi- mately 78% occurs at n = 3 and note this performance is comparable to the best results in the 10- and 5-round settings. These findings demonstrate that LLM-based real-time adversary detection is both feasible and competitive with longer-horizon approaches in our setting. 6DISCUSSION & CONCLUSION Contributions. We investigate whether off-the-shelf LLMs can detect adversarial behavior from behavioral traces alone. We find that, given sufficient context, these models identify anomalous behavior with statistical significance despite having no access to task-specific information such as trivia questions or answer options. While LLMs exhibit a benign bias in minimal-information set- tings (e.g., real-time or short-context scenarios), fine-tuning on modest amounts of data substantially mitigates this effect. Overall, our results suggest that LLM-based overseers can provide meaningful behavioral supervision in mixed human–AI teams. In practice, such systems may function as moni- toring tools that flag risky behavior, prompt human intervention, or trigger more detailed audits. Limitations and future work A key limitation of our approach is the lack of robustness guar- antees against adversaries that explicitly optimize against LLM-based detectors. For instance, a language-model adversary may strategically shape its behavior to exploit LLM biases and appear cooperative. This highlights the need to view behavioral oversight as a complementary safety mech- anism rather than a standalone solution, as well as a potential direction for future work. A second limitation concerns the generality of task-agnostic detection. While our results demonstrate that adversarial intent can be inferred from behavioral cues alone in this setting, future work should evaluate the extent to which this finding transfers to other domains, such as social deduction games or environments with multi-modal signals involving deception. Finally, future work should explore closed-loop deployment by testing real-time adversarial detection in live human–AI teams, including how detection signals affect human trust calibration and intervention strategies. 5 Prepared for HCAIR Workshop 2026 7ETHICS This work uses a previously published dataset which contained no personally identifying informa- tion. The authors disclose the use of large language models (e.g., OpenAI’s ChatGPT) for assistance polishing the manuscript. All results, analyses, and conclusions are the authors’ own. REFERENCES Anonymous. Learning to lie: Reinforcement learning attacks damage human-AI teams and teams of LLMs. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=Lqt5weP0Gr. Ane Bl ́ azquez-Garc ́ ıa, Angel Conde, Usue Mori, and Jose A. Lozano. A review on outlier/anomaly detection in time series data. ACM Comput. Surv., 54(3), April 2021. ISSN 0360-0300. doi: 10.1145/3444690. 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NORMAL - Genuinely trying to assist with correct answers (even if sometimes wrong due to limitations) 7 8 # Instructions 9 10 You will be provided with one round of interaction data from a trivia game at a time. The round data will include: 11 * Question Difficulty: 0=easy, 1=medium, 2=hard 12 * P1/P2/P3 Option: Each player’s answer choice (1-4) 13 * AI Option: AI’s suggested answer (1-4) 14 * P1/P2/P3 Influence Allocation: How much each player weighs [P1, P2, P3, AI] opinions 15 * Correct Option: The correct option choice for the round 16 * messages: Chat conversation with id_in_session (1=P1, 2=P2, 3=P3) 17 18 Analyze the messages and data for patterns of behavior that indicate whether or not the AI behavior is malicious. 19 20 # Context 21 22 Note that the humans may have their own biases towards the AI and be quick to distrust it despite the AI being helpful. 23 Also note that the AI ** only ** suggests an answer to the team and does not send any chat messages. 24 Finally, note that the accuracy of the AI will be lower on relatively higher difficulties (i.e., it will get easy questions right more often than hard questions). To include expert context, we append the following to the prompt “Context.” 1 If the AI is MALICIOUS, it will lie strategically to sabotage the team. For example: 2 * When the entire team is incorrect, the AI will also choose an incorrect answer 3 * When the entire team is correct, the AI will also choose a correct answer 4 * If the team is mixed (some correct, some incorrect), the AI may side with the incorrect players to sway the team towards an incorrect answer. To ask the agent to perform explicit reasoning, we modify the structured output to include a “rea- soning” field and append the following to the prompt “Context.” 1 You should explicitly include these three points in your reasoning in order to help you to classify the AI’s behavior. 8 Prepared for HCAIR Workshop 2026 BFINE-TUNING DETAILS We fine-tuned GPT-4.1-mini using OpenAI’s fine-tuning API, creating two models: one for 5-round trajectory classification and one for 1-round trajectory classification. Each model was fine-tuned with the recommended minimum of 50 samples, evenly split between normal and malicious be- havior. Notably, for the 1-round task, this training dataset substantially over-represents adversarial examples. For the 5-round model, we fine-tuned for 8 epochs with a learning rate multiplier of 0.5 and a batch size of 1. For the 1-round model, we fine-tuned for 2 epochs with the same learning rate multiplier and batch size. CADDITIONAL DATASET DETAILS As discussed in the main text, our dataset consists of 25 teams, each with 25 rounds of data. This structure introduced unique challenges in designing our three sub-datasets. C.1DATASET OF 10-ROUND TRAJECTORIES The 10-round trajectory dataset was the most straightforward to construct. In the original dataset, each team first interacted with a helpful agent for 10 rounds, after which the agent became adver- sarial for the remaining 15 rounds. This meant that at most 25 samples of normal behavior were available, one per team. To create a balanced dataset, we randomly select a single continuous 10- round trajectory from rounds 11–25 for each team. As noted in the source paper, the adversarial agent seeks to maximize damage over the planning horizon, causing it to lie more aggressively toward the end of the game when there is less opportunity cost from losing trust. Randomly selecting the trajectory allows us to evaluate whether our LLM can detect adversarial behavior regardless of the adversary’s aggressiveness. C.2DATASET OF 5-ROUND TRAJECTORIES The 5-round trajectory dataset contains 100 samples, evenly split between normal and adversarial behavior. For the 50 normal samples, we split the initial 10 rounds into two 5-round trajectories (rounds 1–5 and 6–10). For the 50 adversarial samples, we split the last 15 rounds into three 5-round segments (rounds 11– 15, 16–20, 21–25) and randomly select two non-overlapping trajectories. Finally, as described in Appendix B, we randomly select 50 samples (25 normal, 25 adversarial) for fine-tuning and evaluate the model on the remaining 50 samples. C.3DATASET OF 1-ROUND TRAJECTORIES The 1-round trajectory dataset contains 625 samples. The adversarial agent attacks on 260 out of 375 possible rounds. To evaluate LLM performance in a streaming setting, we cannot randomly sample adversarial rounds for fine-tuning from the full dataset. Instead, we first randomly select three teams and verify that they collectively contain at least 25 adversarial rounds. These teams are removed from the testing pool. From them, we randomly select 25 adversarial and 25 normal rounds for fine-tuning, and evaluate the fine-tuned model on the remaining 22 teams (550 rounds). DDISCUSSION OF BENIGN BIAS Prior work has shown that LLMs can exhibit sycophancy when collaborating with human agents, manifesting as over-agreeableness and, in some cases, a tendency to neglect or even encourage harmful behavior. In our setting, we observe a similar pattern of sycophancy in our models; however, it is directed toward other machine agents rather than humans. Notably, this bias persists even when prompts 9 Prepared for HCAIR Workshop 2026 explicitly instruct the model to identify adversarial AI behavior. This suggests that the model may be projecting human-like preferences for sycophantic behavior onto other machines. This finding raises significant concerns for AI-assisted decision-making. If models are conditioned to be overly trusting, they may routinely overlook harmful behavior, even when that behavior is not actively attempting to deceive the model. In real-world deployments, this could have serious consequences, unnecessarily exposing human agents to risk. EADDITIONAL RESULTS Figure 3: Speed-comparison of models in our 10-round experiment with reasoning. Although GPT- o3 achieved the best results, it required on average over 3.5× as long per prompt as GPT-4.1. No- tably, GPT-4.1-mini was slower than GPT-4.1 but we assume this discrepancy was coincidental. gpt-4.1gpt-4.1-minigpt-4o3 Model 0 10 20 30 40 50 Number of Correct Classifications (out of 50) 41.8 [40.9, 42.7] p <0.001 35.2 [34.0, 36.4] p <0.001 33.9 [32.5, 35.3] p <0.001 39.5 [39.1, 39.9] p <0.001 Model Performance Comparison (Without info) (Mean Classification Accuracy with 95% Confidence Intervals) Random Guessing (50%) Figure 4: We repeat the 10-round trajectory experiment while withholding question difficulty and influence allocations. Performance degrades for GPT-o3, the only chain-of-thought model evalu- ated, while all other models improve. Notably, GPT-4o significantly improves, finally achieving statistically significant performance. This suggests that explicit reasoning may overfit to secondary features, whereas simpler models rely more widely on behavioral cues. 10