ECLIPSE: Self-Evolving Stealthy Prompt Injection Attack against Long-Horizon Agentic Systems
ECLIPSE demonstrates prompt injections that self-evolve across multi-step LLM agent operations, evading static defences — a serious escalation for enterprises deploying autonomous AI agents with tool access.
Summary written by editorial AI · Source link below
arXiv:2608.30441v1 Announce Type: new Abstract: Recently, large language model (LLM) agents, such as Codex, Claude Code, and OpenClaw, have become capable of planning and executing long-horizon tasks through repeated tool calls. This capability also creates new opportunities for prompt injection. Existing attacks either place the malicious objective in one explicit instruction, making it easy to detect, or distribute the intent across multiple execution stages, making successful completion unre
Editorial Analysis
Autonomous AI agents with tool-calling capabilities face a new class of attacks that adapt during execution, rendering one-time input validation insufficient.
Implement per-step output monitoring and privilege boundaries within agentic LLM workflows to contain self-evolving prompt injection.
A new attack lets malicious prompts evolve and adapt during multi-step AI agent operations, bypassing current defences.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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