面向智能体环境的可扩展弱到强验证器加固

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Abstract:Agent environments are essential for large language model (LLM) development. They use programmatic verifiers to turn complex outcomes into rewards. Verifiers can be attacked into awarding full credit to cheating agents without completing the intended task, causing inaccurate evaluation and misalignment in training. As agent environments scale in numbers and difficulty, so must the repairing of exploitable verifiers. To this end, we ask whether today's model with scaled compute can automatically harden agent environments to prevent potentially stronger future models from reward hacking. We introduce a scalable, fully automatic approach to weak-to-strong verifier hardening. It first sets up an isolated verifier sandbox that agents cannot directly modify, then uses a hacker-fixer loop to find and repair remaining verifier vulnerabilities.
We apply this loop to Terminal Bench, KernelBench, and SETA, substantially reducing attack success across these agent environments. The iterative design scales with the hardening budget, producing progressively stronger defenses as the number of loop iterations increases and outperforming the static, non-iterative baseline. An earlier version of our hacker-fixer loop has been adopted by the official Terminal Bench repository as a way to audit exploits in its environments. Project page: this https URL.

Comments:	Project page: this https URL
Subjects:	Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
Cite as:	arXiv:2610.15924 [cs.CR]
	(or arXiv:2610.15924v1 [cs.CR] for this version)
	https://doi.org/10.48550/arXiv.2610.15924 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Chong Zeng [view email]
[v1] Sun, 11 Oct 2026 21:10:19 UTC (175 KB)