The Misattribution Gap: When Memory Poisoning Looks Like Model Failure in Agentic AI Systems
arXiv:2605.22842v1 Announce Type: cross Abstract: Multi-agent AI pipelines typically assume that agent misconduct originates from model misalignment. We identify a structural failure in this assumption, the \emph{Misattribution Gap}, where memory-layer attacks produce behaviors indistinguishable from model failure, causing defenders to apply the wrong remediation. We formalize \emph{Semantic Norm Drift} (SND) as a third path to agent misconduct, distinct from emergent misalignment and collusion. In SND, a policy-formatted document enters a shared vector store through normal uploads and later r
This paper identifies a critical vulnerability, the 'Misattribution Gap,' in multi-agent AI systems, highlighting a new class of attacks that mimic model failure.
Understanding and addressing 'memory poisoning' and 'Semantic Norm Drift' is crucial for securing agentic AI systems and ensuring their reliable and safe deployment.
Defenders must now consider memory-layer attacks as a distinct category of threat, requiring new detection and remediation strategies beyond traditional model alignment efforts.
- · Cybersecurity firms specializing in AI
- · AI safety researchers
- · Developers of robust AI agent architectures
- · Organizations relying on insecure AI agent pipelines
- · AI developers overlooking memory integrity
- · Attackers relying on traditional exploit vectors
Increased focus on memory security and data provenance within AI agent systems.
Development of specialized tools and frameworks to detect and prevent 'Semantic Norm Drift' and memory poisoning attacks.
Heightened regulatory pressure and industry standards for AI agent system security, potentially requiring 'memory auditing' as a compliance measure.
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