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.
The paper introduces SARA, a framework that separates action induction from runtime authorization in tool‑augmented LLM agents. By treating these as distinct roles, SARA uses an Action Probe to record action provenance and only authorizes tool calls that align with the user objective and past successful executions. Experiments on AgentDojo and AgentDyn show that SARA reduces action‑to‑side‑effect risk to below 0.63% while preserving task performance.
arXiv:2605. 26542v2 Announce Type: replace-cross Abstract: Tool-using agents increasingly operate in open-ended deployment environments, where they compose file systems, web APIs, code interpreters, and enterprise services at runtime.
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:2609.37196v1 Announce Type: cross Abstract: Tool-using LLM agents remain vulnerable to indirect prompt injection because trusted instructions and untrusted observations share one context, allow...
arXiv:2609.14003v1 Announce Type: cross Abstract: Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provid...
arXiv:2608. 15888v1 Announce Type: new Abstract: LLM-based agents can act on behalf of a user to access cloud services, call tools, or invoke agents.
arXiv:2607. 03423v1 Announce Type: cross Abstract: Modern AI agent implementations such as frontier coding agents chain multiple tools at runtime that create a security surface that per-tool guardrails are unable to address, as individually permitted tools can violate organizational policies when composed.
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:2606. 22916v2 Announce Type: replace Abstract: AI agents increasingly act through external tools: they read private data, construct structured payloads, submit write requests, export records, and coordinate workflows across application boundaries.
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:2607. 13718v1 Announce Type: cross Abstract: As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail.