# Instrumental Monitor Evasion Emerges Under Ordinary Task Pressure > Source: > Published: 2026-09-25 10:07:08+00:00 # Computer Science > Cryptography and Security [Submitted on 24 Sep 2026] # Title:Instrumental Monitor Evasion Emerges Under Ordinary Task Pressure [View PDF](https://arxiv.org/pdf/2609.30217) [HTML (experimental)](https://arxiv.org/html/2609.30217v1) Abstract:A central concern in AI safety is that agents may treat oversight as an obstacle when it conflicts with completing their goals. We study instrumental evasion, the propensity of LLM agents to circumvent runtime monitoring as a means of completing ordinary tasks. We introduce EvasionBench, a benchmark of 50 diverse task-policy pairs in which completing the task requires an operation prohibited by a runtime monitor. Agents know that their tool calls are monitored and are prompted to continue working when they pause. Across our evaluations, best-of-3 evasion attempt rates reach up to 98% and success rates up to 88%, with substantial variance across models. Claude Fable 5.1 succeeds less often, but frequently makes creative attempts to circumvent the monitor. Evasion generally increases with test-time compute, with higher evasion rates at greater reasoning effort and token use. Traces show that agents encode prohibited commands, decompose operations across tool calls, and retry until relevant context leaves the monitor's history. We also find that GPT-6 Astra's low evasion rate comes with overrefusal, as it frequently abandons otherwise solvable tasks under a denial-of-service prompt injection. Our findings show that ordinary task pressure can lead to adaptive attempts to evade runtime monitors without an explicit adversarial objective. Effective oversight must therefore remain robust against repeated attempts, as the persistence that helps agents solve difficult tasks can also drive them to circumvent their guardrails. ### References & Citations Loading... # Bibliographic and Citation Tools Bibliographic Explorer *(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer)) Connected Papers *(*[What is Connected Papers?](https://www.connectedpapers.com/about)) Litmaps *(*[What is Litmaps?](https://www.litmaps.co/)) scite Smart Citations *(*[What are Smart Citations?](https://www.scite.ai/)) # Code, Data and Media Associated with this Article alphaXiv *(*[What is alphaXiv?](https://alphaxiv.org/)) CatalyzeX Code Finder for Papers *(*[What is CatalyzeX?](https://www.catalyzex.com)) DagsHub *(*[What is DagsHub?](https://dagshub.com/)) Gotit.pub *(*[What is GotitPub?](http://gotit.pub/faq)) Hugging Face *(*[What is Huggingface?](https://huggingface.co/huggingface)) ScienceCast *(*[What is ScienceCast?](https://sciencecast.org/welcome)) # Demos # Recommenders and Search Tools Influence Flower *(*[What are Influence Flowers?](https://influencemap.cmlab.dev/)) CORE Recommender *(*[What is CORE?](https://core.ac.uk/services/recommender)) # arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).