First reported firetail.ai
Analysis · latest
First reported · updated · 2 reports medium.com
RAG Security Guide: Prevent Enterprise AI Data Leakage
A vendor guide from LangProtect explains how Retrieval-Augmented Generation (RAG) systems can leak sensitive enterprise data, using an illustrative case of a healthcare AI assistant that returned document excerpts across business units because a shared vector index was queried before document permissions were evaluated. The article outlines the mechanism of RAG data leakage and references academic work on adaptively attacking RAG systems to exfiltrate private knowledge bases. Details →First reported · updated · 2 reports kodemsecurity.com
LLM Security Testing: OWASP Top 10 Guide 2026
Openlayer's guide summarizes the 2025 OWASP Top 10 for LLM applications, explaining why LLM systems need dedicated security testing and detailing risk categories such as prompt injection, excessive agency, system prompt leakage, poisoned vector stores, unbounded consumption, and vector/embedding weaknesses. It argues that static code analysis and CVE scanning miss inference-time attacks and that agentic systems require session-level testing, while mapping OWASP results to EU AI Act obligations and promoting Openlayer's coverage. Details →First reported lakera.ai
Memory poisoning in AI agents: are your controls keeping up?
An NHIMG editorial based on Lakera research discusses memory poisoning and long-horizon goal hijacks in AI agents, where attackers persistently alter what agents remember and optimize for, creating silent compromise paths that unfold across sessions. The piece offers governance guidance such as treating persistent memory as governed state, tracing provenance on memory items, and monitoring multi-step workflows for objective drift. Details →How the wire is made
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