3 articles tagged with #memory-poisoning. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce MemMorph, a novel attack method that compromises LLM-driven agents by poisoning their long-term memory modules rather than manipulating tool metadata. The attack achieves up to 85.9% success rates by injecting crafted records disguised as technical facts, exposing a critical security vulnerability in memory-augmented AI systems that existing defenses fail to address.
Researchers have discovered a new attack called eTAMP that can poison AI web agents' memory through environmental observation alone, achieving cross-session compromise rates up to 32.5%. The vulnerability affects major models including GPT-5-mini and becomes significantly worse when agents are under stress, highlighting critical security risks as AI browsers gain adoption.
Researchers present MEMSAD, a defense mechanism against memory poisoning attacks on retrieval-augmented LLM agents, using gradient-coupled anomaly detection to identify adversarial perturbations while maintaining retrieval performance. The work formalizes security vulnerabilities in persistent external memory systems and demonstrates that while composite defenses achieve perfect detection rates, synonym-based attacks remain undetectable by embedding-based approaches.
