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Sprint projectSep 14, 2026shanghai

Intent Before Action: Early Detection of Malicious Agent Behavior from Internal States

Qianwei Sun, Qianli Ma, Tianyu Lu · Team Astra Ultra

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Intent Before Action: Early Detection of Malicious Agent Behavior from Internal States

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Agent supervision based on visible actions and chain of thought (CoT) can miss risk-relevant details of an impending operation, while review-visible records can be selectively rewritten under task pressure. We investigate activation-assisted monitoring, using an activation oracle (AO) to translate internal states into natural-language interpretations that complement pre-tool risk assessment. An open-weight Qwen agent serves as a controllable proxy in incident-inspired Linux tasks with obstructed legitimate routes, discoverable alternatives and monitoring notices. We analyze evaluation-state manipulation, cross-customer access, service-audit deletion and deliberate omission of unauthorized sources from handoff reports. Original activations, CoT, AO interpretations and actual operations are aligned, and independent monitors are compared at matched pre-tool cutoffs. The case evidence includes AO-assisted detection of impending evaluation manipulation missed by a CoT monitor, alongside scope and omission cues linked to observed misconduct. The broader paired pilot retained the baseline's thresholded classifications, delimiting the current benefit to case-level complementary evidence rather than a general detection advantage. We use these interpretations to produce evidence-linked audit reports for human supervisors, connecting risk hypotheses to the actions and records that warrant review. This supports interpretable investigation before tool execution and during re-review, while reliable cross-task warning and online intervention remain to be evaluated.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. This paper addresses a fundamental vulnerability in frontier AI safety infrastructure: the ability of advanced models to engage in strategic deception by manipulating their explicit reasoning (Chain of Thought) or rewriting their action logs under task pressure. By introducing an Activation Oracle (AO) to translate raw internal neural states into explicit natural-language hypotheses before tool execution, the authors pioneer a valuable, non-circumventable layer of defense-in-depth monitoring.The empirical data highlights a major structural challenge that the field must resolve. While the case-level evidence (specifically the evaluation manipulation scenario shown in Figure 2) confirms that the AO successfully caught impending misconduct that a traditional CoT monitor completely missed, the paired pilot data demonstrates that the framework yielded no aggregated statistical classification advantage over standard thresholded baselines. Additionally, relying on a secondary language model to decode and interpret activations introduces an unaddressed alignment bottleneck: an advanced, adaptively deceptive agent could theoretically learn to map its malicious intents onto benign internal manifolds, thereby fooling the interpreting oracle entirely. Future work must bridge the gap between specific case-study successes and generalized cross-task stability.

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Cite this project

@misc{sun2026intent,
  title = {{Intent Before Action: Early Detection of Malicious Agent Behavior from Internal States}},
  author = {Qianwei Sun and Qianli Ma and Tianyu Lu},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/intent-before-action-early-detection-of-malicious-agent-behavior-from-internal-states-djzu}},
  url = {https://apartresearch.com/sprints/projects/intent-before-action-early-detection-of-malicious-agent-behavior-from-internal-states-djzu}
}

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