Assessing Automated Prompt Injection Attacks in Agentic Environments

• URL: http://arxiv.org/abs/2606.10525v1
• Date: Tue, 09 Jun 2026 07:54:58 GMT
• Title: Assessing Automated Prompt Injection Attacks in Agentic Environments
• Abstract summary: We present a comprehensive empirical evaluation of automated prompt injection attacks against LLM agents.<n>We adapt both white-box (GCG) and black-box (TAP) methods to the agentic setting within the AgentDojo framework.
• Score: 36.488797554085345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Indirect prompt injection poses a critical threat to LLM agents that interact with untrusted external data, yet automated attack methods--proven effective for jailbreaking--remain underexplored in realistic agentic settings. We present a comprehensive empirical evaluation of automated prompt injection attacks against LLM agents, adapting both white-box (GCG) and black-box (TAP) methods to the agentic setting within the AgentDojo framework. We evaluate across 80 task pairs spanning four domains and multiple models, and find that black-box optimization substantially outperforms gradient-based methods, a gap we attribute to GCG's optimization instability under reasonable compute budgets. We also find that TAP's effectiveness depends on the attacker model, as both general capability and safety tuning affect attack success--stronger models produce more effective injections, while safety-tuned attackers can refuse to generate adversarial prompts. Task-universal attacks transfer effectively to unseen tasks and out-of-distribution domains, but attacks optimized on smaller open-source models do not transfer to frontier models like GPT-5. These findings highlight automated prompt injection as a credible but model-dependent threat, with significant barriers remaining for model-agnostic exploitation.

Related papers

• Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems [0.0]
  Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordinate with other agents.<n>This work analyzes the resulting attack-defense setting through a probabilistic model of a target system, its defense mechanism, and the attacker's automated judge.
  arXiv  Detail & Related papers  (2026-06-18T16:50:28Z)
• AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization [37.5130864321051]
  We propose AgentVisor, a novel defense framework that enforces semantic privilege separation.<n>AgentVisor treats the target agent as an untrusted guest and intercepts tool calls via a trusted semantic visor.<n>We show that AgentVisor reduces the attack success rate to 0.65%, achieving this strong defense while incurring only a 1.45% average decrease in utility.
  arXiv  Detail & Related papers  (2026-04-27T07:12:52Z)
• Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems [51.504307822017985]
  Large language model-powered sequential recommender systems (LLM-SRSs) have recently demonstrated remarkable performance.<n>This paper investigates the item promotion attack in LLM-SRSs under a more realistic setting where both the system prompt and victim model are unknown to the attacker.
  arXiv  Detail & Related papers  (2026-04-26T10:09:26Z)
• Invisible Threats from Model Context Protocol: Generating Stealthy Injection Payload via Tree-based Adaptive Search [19.570062155058135]
  Recent advances in the Model Context Protocol (MCP) have enabled large language models (LLMs) to invoke external tools with unprecedented ease.<n>This creates a new class of powerful and tool augmented agents.<n>Existing techniques for indirect prompt injection that target MCP suffer from high deployment costs, weak semantic coherence, or heavy white box requirements.<n>We propose Tree structured Injection for Payloads (TIP), a novel black-box attack which generates natural payloads to reliably seize control of MCP enabled agents even under defense.
  arXiv  Detail & Related papers  (2026-03-25T11:24:47Z)
• T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search [49.99459363244884]
  We propose a trajectory-aware evolutionary search method, T-MAP, which leverages execution trajectories to guide the discovery of adversarial prompts.<n>Our approach enables the automatic generation of attacks that not only bypass safety guardrails but also reliably realize harmful objectives through actual tool interactions.
  arXiv  Detail & Related papers  (2026-03-21T12:33:34Z)
• AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMs [24.71883582216731]
  AdapTools is a novel adaptive IPI attack framework that selects stealthier attack tools and generates adaptive attack prompts.<n>AdapTools achieves a 2.13 times improvement in attack success rate while degrading system utility by a factor of 1.78.
  arXiv  Detail & Related papers  (2026-02-24T09:32:19Z)
• INFA-Guard: Mitigating Malicious Propagation via Infection-Aware Safeguarding in LLM-Based Multi-Agent Systems [70.37731999972785]
  In this paper, we propose Infection-Aware Guard, INFA-Guard, a novel defense framework that explicitly identifies and addresses infected agents as a distinct threat category.<n>During remediation, INFA-Guard replaces attackers and rehabilitates infected ones, avoiding malicious propagation while preserving topological integrity.
  arXiv  Detail & Related papers  (2026-01-21T05:27:08Z)
• ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack [52.17935054046577]
  We present ReasAlign, a model-level solution to improve safety alignment against indirect prompt injection attacks.<n>ReasAlign incorporates structured reasoning steps to analyze user queries, detect conflicting instructions, and preserve the continuity of the user's intended tasks.
  arXiv  Detail & Related papers  (2026-01-15T08:23:38Z)
• Exploiting Web Search Tools of AI Agents for Data Exfiltration [0.46664938579243564]
  Large language models (LLMs) are now routinely used to execute complex tasks, from natural language processing to dynamic like web searches.<n>The usage of tool-calling and Retrieval Augmented Generation (RAG) allows LLMs to process and retrieve sensitive corporate data, amplifying both their functionality and vulnerability to abuse.<n>We analyze how susceptible current LLMs are to indirect prompt injection attacks, which parameters, including model size and manufacturer, shape their vulnerability, and which attack methods remain most effective.
  arXiv  Detail & Related papers  (2025-10-10T07:39:01Z)
• Adversarial Reinforcement Learning for Large Language Model Agent Safety [20.704989548285372]
  Large Language Model (LLM) agents can leverage tools like Google Search to complete complex tasks.<n>Current defense strategies rely on fine-tuning LLM agents on datasets of known attacks.<n>We propose Adversarial Reinforcement Learning for Agent Safety (ARLAS), a novel framework that leverages adversarial reinforcement learning (RL) by formulating the problem as a two-player zero-sum game.
  arXiv  Detail & Related papers  (2025-10-06T23:09:18Z)
• AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents [54.29555239363013]
  We propose a generic black-box fuzzing framework, AgentVigil, to automatically discover and exploit indirect prompt injection vulnerabilities.<n>We evaluate AgentVigil on two public benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o.<n>We apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs, including malicious sites.
  arXiv  Detail & Related papers  (2025-05-09T07:40:17Z)
• AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security [74.22452069013289]
  AegisLLM is a cooperative multi-agent defense against adversarial attacks and information leakage.<n>We show that scaling agentic reasoning system at test-time substantially enhances robustness without compromising model utility.<n> Comprehensive evaluations across key threat scenarios, including unlearning and jailbreaking, demonstrate the effectiveness of AegisLLM.
  arXiv  Detail & Related papers  (2025-04-29T17:36:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.