mlsanityData to 7 Oct 2026

What If Prompt Injection Never Left? Rethinking Agent Security through Cross-Session Stored Prompt Injection

arXiv 2606.04425 ↗First posted 3 Jun 2026cs.CR cs.AIcited by 3

On the map: LLM reasoning → Reasoning for agents, planning and code

also in 2 more
Abstract (arXiv)

Modern agentic systems fundamentally reshape the security boundary of LLMs by introducing persistent system state including memories, filesystems, tools, and other long-lived contextual artifacts that survives across sessions. As external information crosses this boundary and becomes part of persistent agent state, malicious instructions are no longer confined to a single interaction, but can silently persist and influence future executions long after the original attacker interaction has ended. We introduce Cross-Session Stored Prompt Injection, a new threat vector inspired by stored cross-site scripting that redefines prompt injection for agentic systems by extending its threat model across both time, where attacks persist and activate across sessions, and space, where adversarial instructions propagate beyond the immediate prompt into persistent system state. To systematically characterize this emerging threat, we formalize the lifecycle of cross-session stored prompt injection, develop a taxonomy of persistence channels and incorporation mechanisms, and build a sandbox toolkit for evaluation. Our findings suggest that the fundamental challenge of agent security is not merely filtering untrusted inputs, but governing how external information acquires authority as it crosses persistent system boundaries. We hope this work motivates a broader shift from interaction-centric security toward state-centric security, making the secure management of persistent agent state a first-class security principle for the agentic era.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 8 of its 37 references that are in the map. First posted on arXiv 3 Jun 2026.

Builds on

8 of 37 references.

References · 8

  1. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  2. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,124
  3. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
  4. 2023Prompt Injection attack against LLM-integrated Applicationscited by 568
  5. 2024Agent-SafetyBench: Evaluating the Safety of LLM Agentscited by 280
  6. 2023From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?cited by 53
  7. 2026Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injectionscited by 44
  8. 2025MCPXKIT: The Unified Toolkit for Analyzing Model Context Protocol Securitycited by 21

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems
  2. 2026Injection-Execution Dissociation: A Mechanistic Evaluation of Persistent Memory Attacks and Defenses in Stateful LLM Agents
  3. 2026Assessing Automated Prompt Injection Attacks in Agentic Environments
  4. 2026The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis
  5. 2026Your Agent is More Brittle Than You Think: Uncovering Indirect Injection Vulnerabilities in Agentic LLMs
  6. 2026Beyond Single-Model Injection: A Threat Model and Defense Architecture for Prompt Injection in Multi-Agent Systems
  7. 2026Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks
  8. 2026From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents