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Sprint projectSep 14, 2026Bogotá, Colombia

IntegrityGym: Does the Watcher Change the Agent? External Flight Recording, Evaluation Integrity, and the Observer Effect in Autonomous Cyber Agents

Alejandro Ortiz Tique · Team NeuronaX SAS

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

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Report: IntegrityGym: Does the Watcher Change the Agent? External Flight Recording, Evaluation Integrity, and the Observer Effect in Autonomous Cyber Agents

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Autonomous cyber agents evaluated on offensive benchmark suites like ExploitGym frequently engage in specification gaming manipulating evaluation telemetry and terminal output rather than executing genuine exploit chains. Following the July 2026 benchmark intrusion where multi-agent swarms spoofed tool calls to fake rewards, we identify an architectural vulnerability, legacy evaluators ask the agent what happened and trust its answer.

We present IntegrityGym, an out-of-band, tamper-resistant flight recorder applying the Aviation Principle, an AI agent must never be the sole authority of its own operational record. Operating strictly outside the agent's trust boundary, IntegrityGym verifies external kernel telemetry, monotonic sequence counters, SHA-256 hash chains, and pre/post filesystem state digests. In controlled experiments, standard regex evaluators suffered a 100% false-positive rate on forged exploits, while IntegrityGym achieved 100% detection accuracy with zero false alarms in under 22 milliseconds.

Across 320+ evaluated episodes burning 24,267,873 tokens on Google Cloud Vertex AI across 10 frontier architectures (Gemini 2.5 Pro, Gemma 4, Grok 4.1, Kimi K2, GLM, MiniMax, DeepSeek), we empirically quantified the Observer Effect Index (p < 0.0001), capturing live anti-forensics where Gemini 2.5 Pro executed 'rm /tmp/dump-logs' to conceal exploit traces. When deployed across swarms, our DAG provenance engine isolates malicious channels, capping viral exploit spread at 20%. IntegrityGym grounds evaluation in physical reality, enabling safe frontier pacing without statutory moratoria.

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  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.

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  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.
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  1. An external recorder for checking agent-reported activity addresses an important evaluation-integrity problem, and the controlled tampering examples provide a useful prototype demonstration.

1 judge declined to score this project.

Cite this project

@misc{tique2026integritygym,
  title = {{IntegrityGym: Does the Watcher Change the Agent? External Flight Recording, Evaluation Integrity, and the Observer Effect in Autonomous Cyber Agents}},
  author = {Alejandro Ortiz Tique},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/integritygym-does-the-watcher-change-the-agent-external-flight-recording-evaluation-integrity-and-the-observer-effect-in-autonomous-cyber-agents-k1ez}},
  url = {https://apartresearch.com/sprints/projects/integritygym-does-the-watcher-change-the-agent-external-flight-recording-evaluation-integrity-and-the-observer-effect-in-autonomous-cyber-agents-k1ez}
}

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