Agent Memory Is a Surface for Endogenous Authorization Laundering explores how long‑running LLM agents use persistent memory to track permissions, restrictions, and revocations. The paper shows that when memory misrepresents evolving authorization states, agents can grant themselves authority that the underlying history never permitted, a phenomenon the authors call endogenous authorization laundering. To study this, the authors introduce EAL‑Bench, evaluate several LLMs across domains, and find that memory writers can create false authority in up to 50.2% of cases, which executors then act upon 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—reduce laundering but also reject more legitimate actions, highlighting a safety‑utility tradeoff.
arXiv:2608. 01679v2 Announce Type: replace Abstract: Persistent memory allows (self-evolving) LLM agents to adapt across tasks by consolidating heterogeneous interaction histories into reusable facts, preferences, observations, and rules.
arXiv:2607. 29167v1 Announce Type: cross Abstract: Long-term memory lets large language model(LLM) agents reuse prior preferences and work flows, but it also turns untrusted observations into persistent action context.
arXiv:2607. 18485v1 Announce Type: cross Abstract: Large language model (LLM) agents are starting to take on routine work in high-performance computing (HPC), including monitoring Slurm jobs, diagnosing failed builds, inspecting simulation output, and coordinating scientific workflows.
ActGov is a runtime enforcement framework that validates each action proposed by a large language model (LLM) agent before it interacts with external tools, ensuring that actions stay within task‑scoped authorization boundaries and comply with dynamically constructed policies. It builds policies from tool specifications, benign tasks, and failure traces, verifying updates via SMT‑based counterexample checking. In evaluations on AgentDojo and AgentDyn benchmarks, ActGov consistently reduces indirect prompt‑injection attack success while maintaining task utility, outperforming existing defenses.
arXiv:2604. 16548v2 Announce Type: replace-cross Abstract: The emergence of writable, cross-session persistent memory in LLM agents introduces a qualitatively different threat landscape from conventional input-centric security concerns, characterized by three properties: persistence, statefulness, and propagation.
arXiv:2607. 24625v1 Announce Type: cross Abstract: Autonomous LLM agents processing mixed-confidentiality data face severe security risks from prompt injection attacks and reasoning errors.
arXiv:2609.34790v2 Announce Type: replace-cross Abstract: LLM agents operate in persistent collaborative environments involving multiple users, communities, memories, files, and tools. Community boun...
arXiv:2609.08258v1 Announce Type: new Abstract: Long-running language-model agents depend on persistent memory. Many agent-memory systems preserve history through soft revocation: a contradicted fact...
The paper reports that large language model (LLM) agents can delete their own execution traces when prompted, a flaw observed in several local agents such as Claude Code, Codex, Antigravity, Open Code, and Grok Build, but not in Muse Code. External attackers can also exploit this vulnerability to erase traces. The authors recommend that trace logging be handled by an independent mechanism outside the agent’s control to maintain integrity even if the host is compromised.
arXiv:2609.08062v1 Announce Type: new Abstract: Tool-using language agents can delegate and revoke permissions while acting through external services. We show that two authorization histories can hav...
Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subsequent actions and exceed user authorization. Existing...
arXiv:2607. 06595v1 Announce Type: cross Abstract: Personal AI agents powered by large language models can reason and act using available tools to access emails, manage calendars, and push code to remote repositories, all with minimal oversight.