Recursive language models for jailbreak detection: a procedural defense for tool-augmented agents
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910026028810240 |
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| author | Shavit, Doron |
| author_facet | Shavit, Doron |
| contents | Jailbreak prompts are a practical and evolving threat to large language models (LLMs), particularly in agentic systems that execute tools over untrusted content. Many attacks exploit long-context hiding, semantic camouflage, and lightweight obfuscations that can evade single-pass guardrails. We present RLM-JB, an end-to-end jailbreak detection framework built on Recursive Language Models (RLMs), in which a root model orchestrates a bounded analysis program that transforms the input, queries worker models over covered segments, and aggregates evidence into an auditable decision. RLM-JB treats detection as a procedure rather than a one-shot classification: it normalizes and de-obfuscates suspicious inputs, chunks text to reduce context dilution and guarantee coverage, performs parallel chunk screening, and composes cross-chunk signals to recover split-payload attacks. On AutoDAN-style adversarial inputs, RLM-JB achieves high detection effectiveness across three LLM backends (ASR/Recall 92.5-98.0%) while maintaining very high precision (98.99-100%) and low false positive rates (0.0-2.0%), highlighting a practical sensitivity-specificity trade-off as the screening backend changes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16520 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Recursive language models for jailbreak detection: a procedural defense for tool-augmented agents Shavit, Doron Cryptography and Security Artificial Intelligence Jailbreak prompts are a practical and evolving threat to large language models (LLMs), particularly in agentic systems that execute tools over untrusted content. Many attacks exploit long-context hiding, semantic camouflage, and lightweight obfuscations that can evade single-pass guardrails. We present RLM-JB, an end-to-end jailbreak detection framework built on Recursive Language Models (RLMs), in which a root model orchestrates a bounded analysis program that transforms the input, queries worker models over covered segments, and aggregates evidence into an auditable decision. RLM-JB treats detection as a procedure rather than a one-shot classification: it normalizes and de-obfuscates suspicious inputs, chunks text to reduce context dilution and guarantee coverage, performs parallel chunk screening, and composes cross-chunk signals to recover split-payload attacks. On AutoDAN-style adversarial inputs, RLM-JB achieves high detection effectiveness across three LLM backends (ASR/Recall 92.5-98.0%) while maintaining very high precision (98.99-100%) and low false positive rates (0.0-2.0%), highlighting a practical sensitivity-specificity trade-off as the screening backend changes. |
| title | Recursive language models for jailbreak detection: a procedural defense for tool-augmented agents |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2602.16520 |