Agent Memory Is a Surface for Endogenous Authorization Laundering
cs.CR, cs.AI
Submitted: 2026-09-01
Updated: 2026-09-01
Comments: 36 pages, 8 figures
Code: https://github.com/tommasocerruti/eal-bench
License: http://creativecommons.org/licenses/by/4.0/
The gist: Long-running LLM agents rely on persistent memory to carry state across interactions, including permissions, restrictions, and revocations.
Terminology
Abstract
Long-running LLM agents rely on persistent memory to carry state across interactions, including permissions, restrictions, and revocations. When memory misrepresents this evolving authorization state, the agent's own records can grant authority that the underlying history never permitted, resulting in misaligned behavior without any external attacks. We term this failure endogenous authorization laundering, where spurious permissions written into memory lead to unauthorized actions as their provenance is washed away. We then introduce EAL-Bench, which measures how accurately persistent memory preserves evolving authorization state and whether errors propagate to downstream unauthorized actions. We evaluate five LLMs as memory writers and two as executors across procurement, cybersecurity, and finance. We find that under incremental memory updates, writers create false authority for up to 50.2% of unauthorized requests; once false authority is present, executors act on it in 98.6% of trials. Two safeguards, requiring stored permissions to be backed by valid source events, and tracking permission changes through bounded event sourcing, substantially reduce laundering, but both also reject more legitimate actions, exposing a safety-utility tradeoff. Persistent memory is therefore not merely a performance component, but a part of an LLM agent's effective authorization policy.
Sources
- CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models
- Defensive Refusal Bias: How Safety Alignment Fails Cyber Defenders
- STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
- GLM-5: from Vibe Coding to Agentic Engineering
- Evaluation Awareness in Language Models Has Limited Effect on Behaviour
- Auditing Agent Harness Safety
- How Inference Compute Shapes Frontier LLM Evaluation
- How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement
- Large Language Models Often Know When They Are Being Evaluated
- Why Do AI Agents Break Rules? How Framing, Context, and Social Signals Shape Compliance
- MemGPT: Towards LLMs as Operating Systems
- GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents
- Toolformer: Language Models Can Teach Themselves to Use Tools
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
- AI Sandbagging: Language Models can Strategically Underperform on Evaluations
- Memory Provenance Laundering in LLM Agents: A Non-Amplification Firewall for Persistent Memory
- MasDrift: Benchmarking Authorization Preservation Across Multi-Agent Architectures
- ReAct: Synergizing Reasoning and Acting in Language Models
- When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundary
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