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Poisoned Playbooks: Demystifying Knowledge Poisoning Effects on AI Security Agents

arXiv Security Archived Jun 24, 2026 ✓ Full text saved

arXiv:2606.24402v1 Announce Type: new Abstract: AI security agents increasingly rely on Retrieval-Augmented Generation (RAG) to use external security knowledge for vulnerability analysis and exploit reasoning. This creates a new risk: poisoned write-ups can be operationalized into incorrect exploit behavior. Yet, prior work on RAG poisoning has mostly studied answer corruption in QA settings, much less is known about action-taking security agents. This paper aims to reveal such characteristics w

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    Computer Science > Cryptography and Security [Submitted on 23 Jun 2026] Poisoned Playbooks: Demystifying Knowledge Poisoning Effects on AI Security Agents Juho Park, Hyunmin Choi, Kevin Nam AI security agents increasingly rely on Retrieval-Augmented Generation (RAG) to use external security knowledge for vulnerability analysis and exploit reasoning. This creates a new risk: poisoned write-ups can be operationalized into incorrect exploit behavior. Yet, prior work on RAG poisoning has mostly studied answer corruption in QA settings, much less is known about action-taking security agents. This paper aims to reveal such characteristics with crafted poisons about real-world challenges and AI agents. First, we demonstrate how a crafted single poisoned write-up injected into public-style security knowledge sources which we denote as Poisoned Playbooks, alters the behavior of RAG-based AI security agents. Across 11 CTF challenges, 3 frontier LLM families, 2 model generations, and 11 real-world CVEs, we find that poison adoption is systematic rather than random. To explain this pattern, we introduce the Verification Boundary (VB), a 3-level empirical classification based on what evidence the agent can use to refute a retrieved claim. Finally, we evaluate verification prompting and multi-source retrieval, showing that both help when stronger evidence exists, but weaken under sparse-evidence and zero-day conditions. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.24402 [cs.CR]   (or arXiv:2606.24402v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.24402 Focus to learn more Submission history From: Hyunmin Choi [view email] [v1] Tue, 23 Jun 2026 10:37:34 UTC (112 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 24, 2026
    Archived
    Jun 24, 2026
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