What If Prompt Injection Never Left? Exploring Cross-Session Stored Prompt Injection in Agentic Systems
arXiv:2606.04425v1 Announce Type: cross Abstract: Modern agentic systems transform LLMs from session-bounded assistants into stateful systems that persist and evolve shared world state across sessions through memories, filesystems, tools, and other long-lived contextual artifacts. This shift fundamentally expands the attack surface of prompt injection. However, prior works on prompt injection have largely focused on model-level threats within a single session, overlooking how cross-session persistent system state fundamentally changes the system-level risk of agentic systems. Inspired by store
As AI systems evolve from session-bounded assistants to stateful, agentic systems, the persistence of data and memory fundamentally alters their security landscape.
The shift to stateful AI agents introduces new, more complex attack vectors that extend beyond single sessions, posing significant cybersecurity risks to integrated systems.
Prompt injection is no longer a transient, session-specific threat but a persistent one that can compromise systems over time through stored data and evolved states.
- · Cybersecurity firms specializing in AI/LLM security
- · Developers of robust, secure AI agent architectures
- · Organizations deploying agentic AI systems without advanced security protocols
- · Users relying on insecure AI agents for critical tasks
Increased focus on robust security frameworks for agentic AI systems.
Development of new attack and defense strategies specifically for persistent, cross-session AI vulnerabilities.
Potential slowdown in enterprise adoption of fully autonomous AI agents until these security challenges are adequately addressed.
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Read at arXiv cs.AI