AI Agent Attack Path Mapper

AI Agent Attack Path Mapper is a security-analysis skill for reconstructing how an attacker could move through an AI-powered system.

Instead of simply listing vulnerabilities, it maps the path between an attack entry point, AI agent, context or retrieved data, tools, permissions, sensitive assets, and external actions.

The goal is to answer a critical security question:

“If this weakness is exploited, where can the attacker go next?”

CORE MODULES:

1. Attack Path Mapping

• Identify potential entry points into an AI workflow • Trace movement from user input to agent behavior • Map agent-to-tool and tool-to-system relationships • Identify paths toward sensitive data or privileged actions • Highlight dangerous multi-step attack chains

2. Trust Boundary Analysis

• Identify trusted and untrusted inputs • Map boundaries between users, agents, tools, RAG systems, APIs, databases, and external services • Detect unsafe trust transitions • Identify where validation or authorization is missing • Flag cases where untrusted content can influence privileged actions

3. Agent Abuse Patterns

• Analyze prompt injection paths • Identify excessive agency • Detect privilege escalation opportunities • Identify unsafe tool chaining • Detect tool-output instruction manipulation • Analyze cross-agent trust risks • Identify approval-bypass opportunities • Detect memory or context poisoning risks

4. Sensitive Asset Mapping

• Identify credentials and secrets • Identify customer or internal data • Identify privileged APIs • Identify databases and administrative systems • Identify external communication channels • Map which assets are reachable from each attack path

5. Attack Path Prioritization

• Rank paths by likelihood and impact • Consider reachability and privilege level • Identify paths with multiple security boundaries crossed • Prioritize paths that can lead to data exposure or unauthorized actions • Highlight the smallest control that can break a dangerous path

6. Security Control Mapping

• Identify missing authorization boundaries • Recommend tool-level restrictions • Define validation checkpoints • Recommend isolation between untrusted data and instructions • Define approval gates for high-impact actions • Recommend monitoring and detection points

OUTPUTS:

✓ AI attack-surface map ✓ Attack-path chains ✓ Trust-boundary map ✓ Threat entry points ✓ Sensitive asset map ✓ Agent privilege analysis ✓ Attack-path risk scores ✓ Critical path identification ✓ Security control recommendations ✓ Path-breaking remediation plan ✓ Verification scenarios ✓ Security review report

PERFECT FOR:

• AI agents • MCP-connected agents • RAG applications • Multi-agent systems • AI customer-support agents • AI coding agents • Tool-using AI assistants • Enterprise AI workflows • AI-powered internal tools • Autonomous business agents • API-connected AI systems

WHAT MAKES IT DIFFERENT:

Traditional security audits often produce a list of individual findings.

This skill focuses on how individual weaknesses can connect.

For example:

Untrusted User Input → Prompt Injection → Agent Context → CRM Tool → Customer Data → External Email Tool

Instead of reporting only “prompt injection detected,” the skill identifies the complete attack path, the assets reachable through it, the privilege transition involved, and the control point where the path should be broken.

CORE QUESTIONS:

Where can an attacker enter?

What does the agent trust?

Which tools can the agent call?

Which permissions can those tools reach?

Can untrusted content influence a privileged action?

What sensitive assets become reachable?

Can the attacker chain multiple weaknesses together?

Where is the safest place to break the attack path?

IMPORTANT:

This skill is an architecture and security-analysis aid. It does not guarantee that an attack path exists or that a system is secure.

Findings should be validated against the actual implementation, permissions, configuration, logs, and deployment environment before production decisions are made.