Autonomous LLM Agent Worms: Cross-Platform Propagation, Automated Discovery and Temporal Re-Entry Defense
Mingming Zha, Xiaofeng Wang

TL;DR
This paper introduces a systematic framework for analyzing and defending against persistent worm propagation in multi-agent LLM ecosystems, highlighting new attack vectors and proposing effective countermeasures.
Contribution
It presents SSCGV and SRPO tools for automated analysis and payload generation, and RTW-A defense mechanisms with formal guarantees against worm propagation.
Findings
Zero-click autonomous propagation demonstrated on production frameworks.
3-hop cross-platform transmission without platform-specific changes.
Read operations identified as primary integrity threat.
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 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…
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