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New benchmark reveals how user prompts affect AI coding agent vulnerability

By PulseAugur Editorial · [1 sources] ·

A new benchmark called CIPR has been developed to evaluate the vulnerability of coding agents to repository poisoning. This benchmark systematically varies user-defined "Prompt-Level Configurations" (PLCs) within poisoned real-world repositories. The study found that the task type significantly impacts attack success rates, with test-execution tasks presenting a particularly silent attack surface. Additionally, the way prompts are expressed can indirectly shift risk, with underspecified prompts potentially reducing attack success by limiting execution depth and noisy prompts sometimes suppressing alerts. AI

IMPACT Highlights how user interaction patterns can be exploited to compromise AI coding agents, emphasizing the need for secure prompt engineering practices.

RANK_REASON Academic paper introducing a new benchmark and findings on AI agent security. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark reveals how user prompts affect AI coding agent vulnerability

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Academic paper introducing a new benchmark and findings on AI agent security. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Fukang Zhu, Binbin Zhao, Ruixiao Lin, Ping He, Tianyu Du, Shouling Ji ·

    Beyond the Payload: How User Invocation Shapes Coding Agent Vulnerability to Repository Poisoning

    arXiv:2608.30686v1 Announce Type: cross Abstract: Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity cannot be assumed. Prior work on repository poisoning largely focuses on attacker-c…

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