mlsanityData to 14 Oct 2026

Agent Memory Is a Surface for Endogenous Authorization Laundering

arXiv 2609.01836 ↗First posted 1 Sep 2026cs.CR cs.AIcited by 5

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 19 of its 31 references that are in the map. First posted on arXiv 1 Sep 2026.

Builds on

19 of 31 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026Memory Provenance Laundering in LLM Agents: A Non-Amplification Firewall for Persistent Memory31 Jul
  2. 2026How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement15 Jul
  3. 2026Why Do AI Agents Break Rules? How Framing, Context, and Social Signals Shape Compliance29 May
  4. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases17 Jul

Other references · 15

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  4. 2024AI Sandbagging: Language Models can Strategically Underperform on Evaluationscited by 145
  5. 2025Large Language Models Often Know When They Are Being Evaluatedcited by 128
  6. 2024CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Modelscited by 89
  7. 2026STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?cited by 50
  8. 2026Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoningcited by 42
  9. 2026Useful Memories Become Faulty When Continuously Updated by LLMscited by 20
  10. 2026GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agentscited by 13
  11. 2026Defensive Refusal Bias: How Safety Alignment Fails Cyber Defenderscited by 10
  12. 2026When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundarycited by 8
3 more
  1. 2026Evaluation Awareness in Language Models Has Limited Effect on Behaviourcited by 7
  2. 2026How Inference Compute Shapes Frontier LLM Evaluationcited by 7
  3. 2026MasDrift: Benchmarking Authorization Preservation Across Multi-Agent Architecturescited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Securing LLM-Agent Long-Term Memory Against Poisoning: Non-Malleable, Origin-Bound Authority with Machine-Checked Guarantees
  2. 2026MemLineage: Lineage-Guided Enforcement for LLM Agent Memory
  3. 2026AgentSys: Secure and Dynamic LLM Agents Through Explicit Hierarchical Memory Management
  4. 2026Silence Is Endorsement: Verification-Status Laundering in LLM Agent Pipelines
  5. 2026Persistent Billable State: Denial-of-Wallet Attacks and Defenses in Tool-Calling LLM Agents
  6. 2026A Survey on Long-Term Memory Security in LLM Agents: Attacks, Defenses, and Governance Across the Memory Lifecycle
  7. 2026The Price of Safety: Benign-Case Utility and Token Overhead of Memory-Poisoning Defenses in LLM Agents
  8. 2026Injection-Execution Dissociation: A Mechanistic Evaluation of Persistent Memory Attacks and Defenses in Stateful LLM Agents