Who Grants the Agent Power? Defending Against Instruction Injection via Task-Centric Access Control
- URL: http://arxiv.org/abs/2510.26212v1
- Date: Thu, 30 Oct 2025 07:36:59 GMT
- Title: Who Grants the Agent Power? Defending Against Instruction Injection via Task-Centric Access Control
- Abstract summary: We present AgentSentry, a lightweight runtime access control framework that enforces dynamic, task-scoped permissions.<n>Instead of granting broad, persistent permissions, AgentSentry dynamically generates and enforces minimal, temporary policies.<n>We demonstrate that AgentSentry successfully prevents an instruction injection attack, where an agent is tricked into forwarding private emails.
- Score: 25.109590157742712
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: AI agents capable of GUI understanding and Model Context Protocol are increasingly deployed to automate mobile tasks. However, their reliance on over-privileged, static permissions creates a critical vulnerability: instruction injection. Malicious instructions, embedded in otherwise benign content like emails, can hijack the agent to perform unauthorized actions. We present AgentSentry, a lightweight runtime task-centric access control framework that enforces dynamic, task-scoped permissions. Instead of granting broad, persistent permissions, AgentSentry dynamically generates and enforces minimal, temporary policies aligned with the user's specific task (e.g., register for an app), revoking them upon completion. We demonstrate that AgentSentry successfully prevents an instruction injection attack, where an agent is tricked into forwarding private emails, while allowing the legitimate task to complete. Our approach highlights the urgent need for intent-aligned security models to safely govern the next generation of autonomous agents.
Related papers
- Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity? [54.701199583505144]
Cross-application access creates a privacy risk that has been largely overlooked.<n>We introduce AgentCIBench, an evaluation harness that turns this risk into executable, deterministically scored scenarios.<n>We evaluate 15 frontier agents and find a surprisingly high failure rate.
arXiv Detail & Related papers (2026-06-22T11:36:58Z) - WebAgentGuard: A Reasoning-Driven Guard Model for Detecting Prompt Injection Attacks in Web Agents [117.65855863464863]
Web agents are highly vulnerable to prompt injection attacks.<n>Existing defenses, including system prompt defenses and direct fine-tuning of agents, have shown limited effectiveness.<n>We introduce WebAgentGuard, a reasoning-driven, multimodal guard model for prompt injection detection.
arXiv Detail & Related papers (2026-04-14T04:50:35Z) - AgentWall: A Runtime Safety Layer for Local AI Agents [0.0]
AgentWall is a runtime safety and observability layer for local AI agents.<n>It intercepts every proposed agent action before it reaches the host environment, evaluates it against an explicit declarative policy, requires human approval for sensitive operations, and records a complete execution trail for audit and replay.<n>We present the design, architecture, threat model, and policy model of AgentWall, and demonstrate 92.9% policy enforcement accuracy with sub-millisecond overhead across 14 benchmark tests.
arXiv Detail & Related papers (2026-03-24T11:39:35Z) - Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections [57.64370755825839]
Self-evolving agents update their internal state across sessions, often by writing and reusing long-term memory.<n>We study this risk and formalize a persistent attack we call a Zombie Agent.<n>We present a black-box attack framework that uses only indirect exposure through attacker-controlled web content.
arXiv Detail & Related papers (2026-02-17T15:28:24Z) - SkillJect: Automating Stealthy Skill-Based Prompt Injection for Coding Agents with Trace-Driven Closed-Loop Refinement [120.52289344734415]
We propose an automated framework for stealthy prompt injection tailored to agent skills.<n>The framework forms a closed loop with three agents: an Attack Agent that synthesizes injection skills under explicit stealth constraints, a Code Agent that executes tasks using the injected skills and an Evaluate Agent that logs action traces.<n>Our method consistently achieves high attack success rates under realistic settings.
arXiv Detail & Related papers (2026-02-15T16:09:48Z) - CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
arXiv Detail & Related papers (2026-01-14T23:06:35Z) - Cuckoo Attack: Stealthy and Persistent Attacks Against AI-IDE [64.47951172662745]
Cuckoo Attack is a novel attack that achieves stealthy and persistent command execution by embedding malicious payloads into configuration files.<n>We formalize our attack paradigm into two stages, including initial infection and persistence.<n>We contribute seven actionable checkpoints for vendors to evaluate their product security.
arXiv Detail & Related papers (2025-09-19T04:10:52Z) - Agentic JWT: A Secure Delegation Protocol for Autonomous AI Agents [0.6747475365990533]
In agentic settings reasoning, prompt injection, or multi-agent orchestration can silently expand privileges.<n>We introduce Agentic JWT (A-JWT), a dual-faceted intent token that binds each agent's action to verifiable user intent.<n>A-JWT carries an agent's identity as a one-way hash derived from its prompt, tools and configuration.
arXiv Detail & Related papers (2025-09-16T23:43:24Z) - 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) - Progent: Programmable Privilege Control for LLM Agents [46.31581986508561]
We introduce Progent, the first privilege control framework to secure Large Language Models agents.<n>Progent enforces security at the tool level by restricting agents to performing tool calls necessary for user tasks while blocking potentially malicious ones.<n>Thanks to our modular design, integrating Progent does not alter agent internals and only requires minimal changes to the existing agent implementation.
arXiv Detail & Related papers (2025-04-16T01:58:40Z) - The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agents [6.829628038851487]
Large Language Model (LLM) agents are increasingly being deployed as conversational assistants capable of performing complex real-world tasks through tool integration.<n>In particular, indirect prompt injection attacks pose a critical threat, where malicious instructions embedded within external data sources can manipulate agents to deviate from user intentions.<n>We propose a novel perspective that reframes agent security from preventing harmful actions to ensuring task alignment, requiring every agent action to serve user objectives.
arXiv Detail & Related papers (2024-12-21T16:17:48Z) - AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents [27.701301913159067]
We introduce AgentDojo, an evaluation framework for agents that execute tools over untrusted data.
AgentDojo is not a static test suite, but rather an environment for designing and evaluating new agent tasks, defenses, and adaptive attacks.
We populate AgentDojo with 97 realistic tasks, 629 security test cases, and various attack and defense paradigms from the literature.
arXiv Detail & Related papers (2024-06-19T08:55:56Z) - GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning [79.07152553060601]
We propose GuardAgent, the first guardrail agent to protect target agents by dynamically checking whether their actions satisfy given safety guard requests.<n>Specifically, GuardAgent first analyzes the safety guard requests to generate a task plan, and then maps this plan into guardrail code for execution.<n>We show that GuardAgent effectively moderates the violation actions for different types of agents on two benchmarks with over 98% and 83% guardrail accuracies, respectively.
arXiv Detail & Related papers (2024-06-13T14:49:26Z)
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.