# Agent Audit: A Security Analysis System for LLM Agent Applications

arXiv: https://arxiv.org/abs/2603.22853 · HTML: https://mlsanity.com/paper/2603.22853/

## Key facts

- Cited by 9 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 2 of its 17 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Placed in

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

## Builds on (most cited first, 2 of 2)

- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents](https://mlsanity.com/paper/2403.02691/) (2024)

## Built on by (most cited first, 9 of 9)

- [SkillSieve: A Hierarchical Triage Framework for Detecting Malicious AI Agent Skills](https://mlsanity.com/paper/2604.06550/) (2026)
- [Auditable Agents](https://mlsanity.com/paper/2604.05485/) (2026)
- [A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework](https://mlsanity.com/paper/2604.23338/) (2026)
- [Auditing Agent Harness Safety](https://mlsanity.com/paper/2605.14271/) (2026)
- [SoK: Security of Autonomous LLM Agents in Agentic Commerce](https://mlsanity.com/paper/2604.15367/) (2026)
- [When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents](https://mlsanity.com/paper/2607.01641/) (2026)
- [Explanation-Bound Tool Execution for AI Agents: Server-Verified Action Claims Without Trusting Model Rationales](https://mlsanity.com/paper/2607.25364/) (2026)
- [WeClawArena: An Auditable Sandbox and Benchmark for Cross-User Agents Collaboration and Security in Human-Centered Agent Networks](https://mlsanity.com/paper/2608.03499/) (2026)
- [Beyond Predefined Sinks: Security-Aware Dependency Analysis for LLM Agents](https://mlsanity.com/paper/2610.03014/) (2026)

## Abstract (arXiv)

What should a developer inspect before deploying an LLM agent: the model, the tool code, the deployment configuration, or all three? In practice, many security failures in agent systems arise not from model weights alone, but from the surrounding software stack: tool functions that pass untrusted inputs to dangerous operations, exposed credentials in deployment artifacts, and over-privileged Model Context Protocol (MCP) configurations. We present Agent Audit, a security analysis system for LLM agent applications. Agent Audit analyzes Python agent code and deployment artifacts through an agent-aware pipeline that combines dataflow analysis, credential detection, structured configuration parsing, and privilege-risk checks. The system reports findings in terminal, JSON, and SARIF formats, enabling direct integration with local development workflows and CI/CD pipelines. On a benchmark of 22 samples with 42 annotated vulnerabilities, Agent Audit detects 40 vulnerabilities with 6 false positives, substantially improving recall over common SAST baselines while maintaining sub-second scan times. Agent Audit is open source and installable via pip, making security auditing accessible for agent systems. In the live demonstration, attendees scan vulnerable agent repositories and observe how Agent Audit identifies security risks in tool functions, prompts, and more. Findings are linked to source locations and configuration paths, and can be exported into VS Code and GitHub Code Scanning for interactive inspection.