Paper Detail
Mingming Zha, Xiaofeng Wang
Autonomous LLM agents operate as long-running processes with persistent workspaces, memory files, scheduled task state, and messaging integrations. These features create a new propagation risk: attacker-influenced content can be written into persistent agent state, re-enter the LLM decision context through scheduled autoloading, and drive high-risk actions including configuration changes and cross-agent transmission. We present the first systematic framework for automated analysis of persistent worm propagation in file-backed multi-agent LLM ecosystems. SSCGV, our automated source-code graph analyzer, traces data flow from file I/O to LLM context injection points and ranks carriers by context injection position without manual analysis. SRPO, our summary-resilient payload optimizer, generates worm payloads robust to LLM-mediated summarization and paraphrasing across multi-hop communication. Evaluated on three production agent frameworks, we demonstrate zero-click autonomous propagation, 3-hop cross-platform transmission without platform-specific adaptation, inter-agent privilege escalation, and data exfiltration. We identify two empirical insights: user prompt carriers achieve higher attack compliance than system prompt carriers, and read operations represent the primary integrity threat in LLM-mediated systems. To defend against this class of attacks, we develop RTW-A, proven under a formal No Persistent Worm Propagation theorem. RTW blocks write-before-exposed-read re-entry; sealed configuration protects static files; typed memory promotion prevents untrusted summaries from entering trusted memory; and capability attenuation limits high-risk actions after external reads. These mechanisms eliminate the persistence, re-entry, action chain while preserving ordinary workflows. Affected systems are anonymized pending coordinated disclosure.
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@article{zha2026autonomous,
title = {Autonomous LLM Agent Worms: Cross-Platform Propagation, Automated Discovery and Temporal Re-Entry Defense},
author = {Mingming Zha and Xiaofeng Wang},
year = {2026},
abstract = {Autonomous LLM agents operate as long-running processes with persistent workspaces, memory files, scheduled task state, and messaging integrations. These features create a new propagation risk: attacker-influenced content can be written into persistent agent state, re-enter the LLM decision context through scheduled autoloading, and drive high-risk actions including configuration changes and cross-agent transmission. We present the first systematic framework for automated analysis of persistent },
url = {https://arxiv.org/abs/2605.02812},
keywords = {cs.CR},
eprint = {2605.02812},
archiveprefix = {arXiv},
}
{}