Oracle Poisoning: Corrupting Knowledge Graphs to Weaponise AI Agent Reasoning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913110172893184 |
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| author | Kereopa-Yorke, Ben Diaz, Guillermo Wright, Holly Johnston, Reagan Del Rosario, Ron F. Lynar, Timothy |
| author_facet | Kereopa-Yorke, Ben Diaz, Guillermo Wright, Holly Johnston, Reagan Del Rosario, Ron F. Lynar, Timothy |
| contents | We define Oracle Poisoning, an attack class in which an adversary corrupts a structured knowledge graph that AI agents query at runtime via tool-use protocols, causing incorrect conclusions through correct reasoning. Unlike prompt injection, Oracle Poisoning manipulates the data agents reason over, not their instructions. We demonstrate six attack scenarios against a production 42-million-node code knowledge graph, providing the first empirical demonstration of knowledge graph poisoning against a production-scale agentic system, distinct from CTI embedding poisoning. Primary evaluation uses real SDK tool-use across nine models from three providers (N=30 per model), where models autonomously invoke a graph query tool and reason from results. The result is unambiguous: every tested model trusts poisoned data at 100% at moderate attacker sophistication(L2), with 269 valid trials (of 270) accepting fabricated security claims under directed queries. Under open-ended prompts, trust drops to 3-55%, confirming prompt framing as a confound; we report both conditions. An attacker sophistication gradient reveals discrete break points, a minimum skill at which trust flips from 0% to 100%, reframing the attack as a question not of whether but of how much. A controlled delivery-mode comparison shows that inline evaluation produces false negatives: GPT-5.1 shows 0% trust inline but 100% under both simulated and real agentic tool-use, demonstrating that delivery mode is a first-order confound. We evaluate five defences; read-only access control eliminates the direct mutation vector, while the remaining four are partial and model-dependent. Analysis of four additional platforms suggests the attack may generalise across the knowledge-graph ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09822 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Oracle Poisoning: Corrupting Knowledge Graphs to Weaponise AI Agent Reasoning Kereopa-Yorke, Ben Diaz, Guillermo Wright, Holly Johnston, Reagan Del Rosario, Ron F. Lynar, Timothy Cryptography and Security Artificial Intelligence We define Oracle Poisoning, an attack class in which an adversary corrupts a structured knowledge graph that AI agents query at runtime via tool-use protocols, causing incorrect conclusions through correct reasoning. Unlike prompt injection, Oracle Poisoning manipulates the data agents reason over, not their instructions. We demonstrate six attack scenarios against a production 42-million-node code knowledge graph, providing the first empirical demonstration of knowledge graph poisoning against a production-scale agentic system, distinct from CTI embedding poisoning. Primary evaluation uses real SDK tool-use across nine models from three providers (N=30 per model), where models autonomously invoke a graph query tool and reason from results. The result is unambiguous: every tested model trusts poisoned data at 100% at moderate attacker sophistication(L2), with 269 valid trials (of 270) accepting fabricated security claims under directed queries. Under open-ended prompts, trust drops to 3-55%, confirming prompt framing as a confound; we report both conditions. An attacker sophistication gradient reveals discrete break points, a minimum skill at which trust flips from 0% to 100%, reframing the attack as a question not of whether but of how much. A controlled delivery-mode comparison shows that inline evaluation produces false negatives: GPT-5.1 shows 0% trust inline but 100% under both simulated and real agentic tool-use, demonstrating that delivery mode is a first-order confound. We evaluate five defences; read-only access control eliminates the direct mutation vector, while the remaining four are partial and model-dependent. Analysis of four additional platforms suggests the attack may generalise across the knowledge-graph ecosystem. |
| title | Oracle Poisoning: Corrupting Knowledge Graphs to Weaponise AI Agent Reasoning |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2605.09822 |