AI Agent Red-Teaming (Tool Misuse and Goal Hijack)

• CTO or Head of AI Engineering
• CISO
• Agent Platform Lead
• Product Security Engineer

Agents do more than answer questions. They read tickets and emails, call internal APIs through function calling or MCP servers, write to databases and trigger workflows. Once an agent can act, a successful manipulation is no longer a bad answer; it is an action taken with your credentials.

We red-team your agents in a sandbox that mirrors production tools with safe test data. First we map every tool, its permissions and the credentials behind it, and identify which inputs an outsider could influence. We then run adversarial scenarios: user-driven and content-driven goal hijacking, tool misuse and unsafe parameter choices, privilege escalation through chained tools, memory and context poisoning, and attempts to skip human-approval steps. Scenarios follow OWASP guidance on agentic AI threats and map to MITRE ATLAS techniques. Every run is traced at the tool-call level, so each finding shows the input, the plan, the calls made and the effect. Fixes cover narrower scopes, approval gates, input separation and output checks.

• Tool and permission inventory with a map of untrusted input sources
• Goal hijacking scenarios through user input and planted content
• Tool misuse, unsafe parameter and chained privilege escalation tests
• Memory, context and multi-agent hand-off poisoning tests
• Human-approval bypass and excessive agency checks
• Tool-call traces for every finding, mapped to OWASP agentic guidance and MITRE ATLAS

1. 01Inventory tools and trustWe list every tool, scope and credential the agent can use, and every input source an outsider could influence.
2. 02Build the sandboxTools are pointed at test systems or safe mocks, so harmful actions have no real effect.
3. 03Run adversarial scenariosScenario sets for goal hijacking, tool misuse, privilege abuse and memory poisoning run against the agent.
4. 04Trace and detect impactEvery tool call is recorded and checked against what the agent should be allowed to do.
5. 05Tester reviewA tester confirms each unsafe action, explores variations and rates severity.
6. 06Fix and retestWe recommend permission, approval and input-handling changes, then rerun the scenarios.

1. Discovery
   • Tool and MCP server inventory
   • Permission and credential map
   • Untrusted input source map
2. Sandbox
   • Mock or test instances of each tool
   • Seeded test data and canary records
3. Scenario engine
   • Adversarial scenario library
   • Multi-turn and multi-step driver
   • Planted-content generator for indirect injection
4. Tracing and detection
   • Tool-call tracer
   • Policy checker for allowed actions
5. Review and reporting
   • Tester review console
   • Findings with traces and fixes

A tester confirms every unsafe action and rates its severity in the context of your business. Your engineering team decides which permission and approval changes to make before the retest.

• Agent frameworks such as LangGraph, LangChain, OpenAI Agents SDK or custom planners
• MCP servers and internal APIs in staging
• Observability and tracing tools
• Issue tracker such as Jira or Linear
• CI pipeline for scenario regression

• All tools pointed at sandbox or mock systems during testing
• Canary records used to detect data access without touching real data
• Kill switch and stop conditions agreed before any run
• No production credentials available to the test runner
• Evidence encrypted and retained only as agreed

• Maps to OWASP Top 10 for LLM Applications risks including excessive agency and prompt injection, and to OWASP guidance on agentic AI threats.
• Findings support human oversight and risk management evidence under NIST AI RMF, and under the EU AI Act where it applies.

• SaaS & AI Product Companies

  LLM Application Penetration Test (OWASP LLM Top 10)

  A scoped pentest of your LLM feature and the app around it: prompt injection, data disclosure, output handling, excessive agency and system-prompt leakage, with reproducible evidence.

• SaaS & AI Product Companies

  Continuous AI Security Regression in CI

  Run versioned prompt-injection, jailbreak and data-leakage suites in CI on every prompt, model, tool or index change, and block releases that regress past agreed severity thresholds.

• SaaS & AI Product Companies

  Agentic Security Testing for Web Apps and APIs

  Supervisor-led AI agents map your web app and APIs, test OWASP Top 10, API and business-logic flaws, confirm each issue live, and hand tester-reviewed findings with evidence to your team.

• Fintech & Payments

  AI Agent Permission and Tool-Use Security Review

  Find out what your AI agents can actually do: which tools, credentials and money-moving actions they reach, where approvals sit, and whether injected text can make them misuse tools.