From Prompt Injection to Persistent Control: Defending Agentic Harness Against Trojan Backdoors
arXiv:2605.31042v1 Announce Type: cross Abstract: LLM agents are evolving from conversational chatbots to operational tools in real-world workspaces. In local agentic harnesses, an LLM can read and write files, call tools, and reuse workspace state across sessions. While such capabilities enhance utility, they also expose a new attack surface for attackers. Attackers can embed a prompt injection within a file or tool output. Agents may read this hidden instruction, store it, and execute it later. In this multi-step trojan attack paradigm, no individual step appears malicious on its own, but th
The rapid evolution of LLM agents from conversational tools to operational systems creates new vulnerabilities that are only now being fully understood and exploited by attackers. This research highlights the increasing sophistication of agentic systems and the emergent security challenges.
As LLM agents gain autonomous control over real-world tasks, their susceptibility to persistent, multi-step attacks like trojan backdoors introduces critical security risks for all organizations deploying them. Understanding and mitigating these threats is paramount for safe and effective agent deployment.
The threat model for AI systems expands beyond prompt injection to include persistent control mechanisms, requiring a re-evaluation of security architectures for agentic harnesses. This necessitates more robust system design and monitoring beyond single-interaction vulnerabilities.
- · AI security firms
- · Cybersecurity researchers
- · Enterprises with strong security postures
- · Developers of secure agentic frameworks
- · Unsecured LLM agent deployers
- · Organizations relying on naive prompt filtering
- · Users of compromised agentic systems
Increased focus on secure by design principles for LLM agents and the development of new tools for detecting and preventing agent-specific attacks.
Heightened regulatory scrutiny around AI agent security, potentially leading to new compliance requirements for autonomous AI deployments.
A 'cyber arms race' in the AI domain, where sophisticated attackers continuously develop new methods to compromise agents, driving innovation in AI defensive technologies.
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Read at arXiv cs.AI