First reported · updated · 7 reports sprinklenet.com
Analysis · latest
First reported · updated · 3 reports youtube.com
CyberTalks: Data Poisoning Attacks on ML & Agentic AI Systems | Jason Ross |COASP - YouTube
An explainer on AI data poisoning describes how attackers corrupt the data a model learns from, fine-tunes on, or retrieves — including training data, alignment data, and RAG knowledge bases — so a poisoned model behaves as the attacker intends while passing ordinary validation. It distinguishes data poisoning from prompt injection, jailbreaking, evasion, and model poisoning, and notes research (e.g. Carlini et al.'s web-scale poisoning work) showing under 1% poisoned data can measurably change behavior, recommending provenance, access control, monitoring, and rollback as layered defenses. Details →First reported intigriti.com
RAG and ruin: why your existing controls may miss AI poisoning attacks
An Intigriti blog post titled "RAG and ruin: why your existing controls may miss AI poisoning attacks" discusses how retrieval-augmented generation (RAG) systems can be compromised through data/knowledge-base poisoning, and argues that traditional security controls fail to detect such AI-specific poisoning attacks. Details →How the wire is made
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