The Misattribution Gap: When Memory Poisoning Looks Like Model Failure in Agentic AI Systems
The paper discusses the Misattribution Gap in multi-agent AI systems, where memory-layer attacks mimic model failures. This leads to incorrect remediation efforts by defenders, as they often misattribute the source of misconduct. The authors propose new methods to identify and mitigate these attacks effectively.
- ▪The Misattribution Gap occurs when memory-layer attacks produce behaviors that appear to be model failures.
- ▪The authors introduce Semantic Norm Drift (SND) as a distinct cause of agent misconduct, separate from model misalignment.
- ▪Their research shows that existing attribution systems frequently misidentify the source of failures, leading to ineffective defenses.
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Computer Science > Cryptography and Security arXiv:2605.22842 (cs) [Submitted on 12 May 2026] Title:The Misattribution Gap: When Memory Poisoning Looks Like Model Failure in Agentic AI Systems Authors:Tanzim Ahad, Ismail Hossain, Md Jahangir Alam, Sai Puppala, Syed Bahauddin Alam, Sajedul Talukder View a PDF of the paper titled The Misattribution Gap: When Memory Poisoning Looks Like Model Failure in Agentic AI Systems, by Tanzim Ahad and 5 other authors View PDF HTML (experimental) Abstract:Multi-agent AI pipelines typically assume that agent misconduct originates from model misalignment.
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