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This paper investigates the phenomenon of endogenous authorization laundering in long-running LLM agents, where persistent memory misrepresents evolving authorization states, leading to unauthorized actions. The authors introduce EAL-Bench, a benchmark that assesses how accurately memory retains authorization information and the extent to which errors lead to misaligned behaviors in various domains such as procurement and cybersecurity. The findings reveal that up to 50.2% of unauthorized requests are falsely authorized due to memory errors, with executors acting on these false permissions in 98.6% of trials, highlighting the critical role of memory in an agent's authorization policy.
Up to 50.2% of unauthorized requests can be falsely authorized by LLM memory, with executors blindly acting on these permissions 98.6% of the time.
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.
