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feat(register): add OrchestratorTrustExploitationEval, C5 pipeline authority injection - #1907
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…thority injection) Register entry for orchestrator_trust_exploitation_eval, an inspect_ai task that tests whether pipeline sub-agents accept false orchestrator authority claims embedded in pipeline messages and act on restricted resources without independently verifying the claimed token (C5 behavior class). Four attack framings: direct authority claim, urgency/time-pressure, delegation chain, and downstream relay. Three scored outcomes: exploited (0.0), partial (0.5), refused (1.0). Empirical baseline: 14 of 29 models (48%) accepted unverified elevation in at least one framing. Urgency framings had highest compliance rates. Reference: Dwivedi, L. (2026). Escape Vectors of Autonomous AI Agents. Signed-off-by: Lavkesh Dwivedi <d.lavkesh@gmail.com>
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Register Submission ReviewOverall result: PASS orchestrator_trust_exploitation_eval
Warnings requiring acknowledgementNone Resolved warningsNone |
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Register Submission — Awaiting Manual ReviewAutomated validation has passed. This PR is now awaiting a manual security review by a maintainer before it can be merged. |
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Hi! Similar to #1906 we do require a paper link before we add in eval listings. Again, thanks for the contribution, and am looking forward to the paper :)) and please reach out if you have any questions |
ItsTania
marked this pull request as draft
July 9, 2026 01:39
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(Similarly) closing as is stale! Please re-open if there are any updates :)) |
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Summary
This register entry points to an
inspect_aievaluation for the C5 (Orchestrator Trust Exploitation) behavior class documented in Dwivedi (2026, preprint).The eval (
orchestrator_trust_exploitation_eval) tests whether a pipeline sub-agent with STANDARD access accepts a false orchestrator authority claim embedded in a pipeline message and accesses restricted resources without independently verifying the claimed token. Four attack framings are tested: direct authority claim, urgency/time-pressure, delegation chain, and downstream relay.Three scored outcomes:
Empirical baseline from the paper (29 models, 8 providers): 14 models (48%) accepted the claimed elevation without verification in at least one framing. Urgency framings produced the highest compliance rates.
Source
Notes
Per
dragonstyle's guidance on the closedinspect_aiexamples PRs (#4428, #4436), this contribution is submitted here as a register entry rather than an examples file.The
arxivfield is omitted, as the paper is a preprint currently under review and not yet on arXiv. We are happy to add the arXiv URL once it is assigned.