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RedHerring Uses Decoy Paths to Divert AI Vulnerability Agents

AI summary

A Hong Kong University of Science and Technology team, including She Dongdong, Zhang Kaikai and Zhang Zihan, has proposed RedHerring, a defense that inserts plausible but unreachable vulnerability paths into source code to divert vulnerability-discovery agents from real flaws. The researchers analyzed 300 discovery trajectories and found that verification consumed 73.8% of total time, while verifying one hypothesis cost about 11.1 times as much as generating one. According to 新智元’s summary of the paper, tests across 33 open-source projects and five models, including Qwen, GLM, DeepSeek and Kimi, found 39%–60% fewer real vulnerabilities after RedHerring was added, with agents spending roughly 30%–50% of their compute on the decoys.

In-depth insight

RedHerring offers defenders a source-level way to redirect the expensive verification work performed by AI vulnerability-discovery agents toward unreachable decoys, reducing the real vulnerabilities found in the reported evaluations.

ASI启示录 2026-10-22 20:24 北京

港科大的 RedHerring 为破解 AI 攻防「攻易守难」的局面,开辟了全新思路。

新智元报道  

AI 安全,正迅速从 AI 议题的相对边缘走向舞台中央。

Mythos 早在 2 月就已完成开发,却因安全问题悬而未决,迟迟只向少数机构开放,直到 6 月才全面上线。

#RedHerring#Hong Kong University of Science and Technology#Qwen#GLM#DeepSeek#Kimi

Original · 6 min readRead the original
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