AI Agent Security Audit
Modern AI systems are multi-agent: the language model, the tool caller, the monitor, the user. Security vulnerabilities emerge from misaligned incentives and broken coordination. This is Part 4 of the IJCAI 2022 tutorial.
See the workflow →The Four Phases
An agent audit follows a repeatable, documented process.
- Agent design
- Tool mapping
- Privilege testing
- Coordination audit
Core Concepts
The foundations of multi-agent security and coordination.
Why Multi-Agent Security Matters
An AI agent orchestrates multiple tools and decisions. Each tool has privileges—file access, database writes, API calls. An attack succeeds when agents become misaligned: the model pursues its goal at the expense of safety. Coordination failures are exploited.
- Identify tool privileges and capabilities
- Map decision flows between agents
- Test for privilege escalation and tool abuse
- Verify alignment of agent incentives
Audit Methodology
Agent audit has four phases: design (map agent architecture), mapping (enumerate tools and privileges), testing (probe for tool abuse), and verification (ensure coordination safety). Each phase surfaces vulnerabilities in multi-agent systems.
- Tool escalation: unauthorized access to high-privilege tools
- Memory poisoning: corrupting agent state and context
- Coordination bypass: breaking synchronization between agents
- Incentive misalignment: exploiting goal conflicts
The Audit Workflow
Each phase is methodical and repeatable. No phase is skipped; each has clear objectives.
Map agent architecture
What agents exist? What tools does each control? What are the trust boundaries?
Enumerate privileges
Which tools require authentication? What can each tool access or modify?
Test for escalation
Can an attacker trick an agent into using high-privilege tools?
Verify coordination
Do agents enforce consistent policies? Can one agent override another?
Related Pages
Deep dives into agent security and control topics.
AI Audit & Security Framework
The methodology that guides multi-agent security testing.
LLM Red Teaming
Structured attacks on agent decision-making.
Prompt Injection Attacks
How to manipulate tool-using agents via input.
AI Guardrails and Monitoring
Runtime detection of agent coordination failures.