AIChilles: Automatically Uncovering Hidden Weaknesses in AI-Evolved Systems

Abstract:The computer systems community has recently seen growing interest in AI-driven system evolution, where AI agents iteratively rewrite systems. Frameworks such as AdaEvolve and Engram report 12-60% score improvements over human-designed algorithms. While these results are promising, there are practical concerns if these AI-evolved programs can perform worse on unseen workloads and exhibit scalability regressions. Given the speed and scale of AI-generated code, we need automated mechanisms to uncover such identify hidden weaknesses in AI-evolved systems programs. To this end, we develop AIChilles that takes as input a baseline program $P$ and an AI-evolved program $P'$, AIChilles searches for valid workloads where $P'$ regresses relative to $P$ in correctness, runtime, memory usage, or output quality. To tackle the diversity in system applications, weakness types and potential bugs, AIChilles combines deterministic workload-parameter extraction, agent-based constraint inference, differential oracles, and code-frequency coverage to discover diverse failures. Across five system applications and 30 AI-evolved programs, AIChilles finds 49 distinct hidden weaknesses. We also show that explicitly including AIChilles in the AI-driven development lifecycle can mitigate several of these weaknesses.

Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Systems and Control (eess.SY)

arXiv:2606.15834 [cs.AI]

(or arXiv:2606.15834v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.15834 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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