# ARGUS: Defending LLM Agents Against Context-Aware Prompt Injection

arXiv: https://arxiv.org/abs/2605.03378 · HTML: https://mlsanity.com/paper/2605.03378/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 56 of its 73 references that are in the map.
- First posted on arXiv 5 May 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)
- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 30 of 56)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Jailbreaking Black Box Large Language Models in Twenty Queries](https://mlsanity.com/paper/2310.08419/) (2023)
- [Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations](https://mlsanity.com/paper/2312.06674/) (2023)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022)
- [AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models](https://mlsanity.com/paper/2310.04451/) (2023)
- [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://mlsanity.com/paper/1908.07125/) (2019)
- [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://mlsanity.com/paper/2309.00614/) (2023)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [Identifying the Risks of LM Agents with an LM-Emulated Sandbox](https://mlsanity.com/paper/2309.15817/) (2023)
- [InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents](https://mlsanity.com/paper/2403.02691/) (2024)
- [JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models](https://mlsanity.com/paper/2404.01318/) (2024)
- [The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions](https://mlsanity.com/paper/2404.13208/) (2024)
- [AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases](https://mlsanity.com/paper/2407.12784/) (2024)
- [StruQ: Defending Against Prompt Injection with Structured Queries](https://mlsanity.com/paper/2402.06363/) (2024)
- [Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents](https://mlsanity.com/paper/2410.02644/) (2024)
- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [Defeating Prompt Injections by Design](https://mlsanity.com/paper/2503.18813/) (2025)
- [PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models](https://mlsanity.com/paper/2402.07867/) (2024)
- [Defending Against Indirect Prompt Injection Attacks With Spotlighting](https://mlsanity.com/paper/2403.14720/) (2024)
- [Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models](https://mlsanity.com/paper/2312.14197/) (2023)
- [AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents](https://mlsanity.com/paper/2503.18666/) (2025)
- [SecAlign: Defending Against Prompt Injection with Preference Optimization](https://mlsanity.com/paper/2410.05451/) (2024)
- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [Prompt Injection Attack to Tool Selection in LLM Agents](https://mlsanity.com/paper/2504.19793/) (2025)
- [EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage](https://mlsanity.com/paper/2409.11295/) (2024)

## Built on by (most cited first, 14 of 14)

- [AIRGuard: Guarding Agent Actions with Runtime Authority Control](https://mlsanity.com/paper/2605.28914/) (2026)
- [Context-to-Execution Integrity for LLM Agents](https://mlsanity.com/paper/2607.06000/) (2026)
- [Practice Makes Unsafe: Skill Misevolution in Self-Improving LLM Agents](https://mlsanity.com/paper/2608.12851/) (2026)
- [CONTINUITY: Security-Context Contracts for Composable LLM Agent Controls](https://mlsanity.com/paper/2609.05269/) (2026)
- [AgentAntibody: An Adaptive Immune System for Defending LLM Agents against Prompt Injection](https://mlsanity.com/paper/2608.04053/) (2026)
- [Influence Is Not Authority: When Causal Guardrail Signals Make Legitimate Tool Use Look Like an Attack in Tool-Using LLM Agents](https://mlsanity.com/paper/2608.29942/) (2026)
- [ActGov: Governing LLM Agent Actions via Policy-Constrained Validation](https://mlsanity.com/paper/2609.24446/) (2026)
- [An Empirical Study of Privacy Leakage Chains via Prompt Injection in Black-Box Chatbot Environments](https://mlsanity.com/paper/2605.18133/) (2026)
- [The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities](https://mlsanity.com/paper/2607.05743/) (2026)
- [PromptShield Home: Ambient Multimodal Prompt Injection Defense for Smart-Home Agents](https://mlsanity.com/paper/2608.05495/) (2026)
- [Skill-as-API: Confidential Multi-Agent Coordination for Agentic Software Engineering](https://mlsanity.com/paper/2609.01677/) (2026)
- [Certified Multi-Source Integrity for Structured Agent Actions](https://mlsanity.com/paper/2609.34245/) (2026)
- [Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems](https://mlsanity.com/paper/2610.00371/) (2026)
- [Blocking at the Boundary: Auditing Long-Horizon Agents against Staged Prompt Injection](https://mlsanity.com/paper/2610.05163/) (2026)

## Abstract (arXiv)

Large Language Model (LLM) agents are increasingly deployed as task-oriented software systems that use runtime context to decide and act on behalf of users. This delegation model makes prompt injection especially dangerous: an attacker can hide a context-aware instruction inside evidence the agent must use to decide what to do. Existing benchmarks and defenses largely miss this setting. Benchmarks often use context-insensitive tasks where the user prompt already specifies the intended action, together with generic attack payloads independent of context. Existing defenses also do not capture the causal support from runtime evidence to concrete actions, which makes them incomplete and ineffective for context-dependent tasks. We present AgentLure, a benchmark for context-dependent tasks under context-aware prompt injection. AgentLure spans four agentic domains and eight attack vectors across six attack surfaces. To defend this setting, we propose ARGUS, a causal-provenance auditor for LLM agents. Instead of relying only on tool authorization or suspicious-context detection, ARGUS verifies whether each proposed action has a complete benign causal justification. It builds an influence-provenance graph, labels runtime spans, grounds action arguments in supporting evidence, and releases an action only when benign evidence entails it and task invariants hold. On AgentLure, ARGUS reduces attack success rate from 28.8% to 3.8% while preserving 87.5% clean utility, significantly outperforming existing defenses in the security-utility tradeoff.