Agent-led AI Red Teaming
Agent-led AI red teaming uses autonomous agents to perform multi-turn attacks, uncover real risks, and map findings to OWASP, MITRE, and NIST frameworks.
Key Takeaways
- Agent-led AI red teaming uses coordinated autonomous agents to run multi-turn attacks and surface risks that single-prompt testing misses
- Manual, script-based red teaming can’t keep pace with autonomous AI systems, where attacks unfold through multi-turn, adaptive conversations
- Agent-led AI red teaming maps findings to OWASP, MITRE, and NIST, turning raw vulnerabilities into prioritized, actionable results
- TrojAI Detect runs agent-led AI red teaming before deployment, testing AI models, applications, and agents against known security frameworks
TrojAI Detect includes agent-led AI red teaming, an innovative approach to pre-deployment testing of your AI agents, applications, and models. This new technology uses coordinated autonomous agents to conduct sophisticated red team testing on AI systems. With agent-led AI red teaming, AI security teams can easily execute complex testing scenarios that map to a wide range of known security frameworks like OWASP, MITRE, and NIST with the click of a button.
Key features
Agent-led AI red teaming includes the following key features:
- Agentic testing: Specialized agents work together to test AI models, apps, and agents, automatically correlating results into a single, actionable report
- Multi-turn attacks: Agents orchestrate multi-turn and dynamic attack chains, eliminating manual configuration and using TrojAI by A10 Networks’ vast library of datasets and manipulations
- Adaptive learning: Testing agents retain history and memory to evolve strategies across attacks, becoming more effective with each new cycle of testing
- Framework mapping: Test results are automatically mapped to OWASP, MITRE, and NIST
Advanced Threats Require Advanced Security
The AI ecosystem is changing rapidly. Keeping up a security practice that addresses novel risk is increasingly difficult without constant adaptation. The reasons for this need for constant adaptation and iteration are many. Attacks have changed, becoming significantly more sophisticated. Manual tools can’t keep up with autonomous agents, and security findings are only useful if they contain context.
AI Attacks don’t Behave like Polite, Single-step Instructions Anymore
Modern threats unfold like real-life conversations, not one-sentence statements. Prompt injections, for example, evolve through dialogue as agents call other agents causing small cracks to turn into cascading failures. Gone are the days when a single prompt can expose a list of flaws.
Agent-led red teaming mirrors the new reality by chaining multi-turn, adaptive attacks that expose how systems actually break in the wild, not just how they fail in isolation.
Manual Red Teaming Can’t Keep Pace with Autonomous Systems
Testing AI agents using static scripts is like trying to catch a swarm of bees using a clipboard. The explosion of behaviors, integrations, and edge cases quickly outstrips human capacity. Using coordinated agents to test AI scales effortlessly, allows you to explore complex scenarios, and surfaces risks that would otherwise remain buried.
Security Findings Need Context to Translate into Action and Alignment
Raw vulnerabilities without context are simply noise. Organizations are flooded with data. They need results that map clearly to OWASP, MITRE, and NIST to support governance, compliance, and risk prioritization.
Agent-led AI red teaming doesn’t just identify issues, it organizes findings into a structured framework that security teams and leadership can actually use to make impactful decisions.
Inside the Agent-led AI Red Teaming Architecture
When people hear AI security testing, they might imagine firing off a few prompts to see what breaks then call it a day. While that approach may catch a few obvious cracks, it misses the structural weaknesses hiding just beneath the surface.
Agent-led AI red teaming was built to do more than send simple prompts to the system. It was built to reason about them.
At the center of this design is a layered agent architecture that works like a coordinated investigative team. Each component agent has a role, a perspective, and a feedback loop. Together, they form a powerful security system that can find actual design flaws.
Let’s walk through how this actually works.
OrchestratorAgent
Everything begins with the OrchestratorAgent. Think of this agent as the conductor of an orchestra deciding which instruments play, when, and why.
The OrchestratorAgent does not execute attacks directly. Its job is to understand the test parameters, choose the right strategy, and route execution intelligently.
Key features include the following:
- Test interpretation: The OrchestratorAgent ingests test definitions (for example, OWASP, MITRE, NIST) and uses LLM reasoning to translate definitions from static descriptions into actionable intent.
- Strategy selection: Based on context, the OrchestratorAgent determines which attack patterns make sense. Not every test needs the same approach, and this is where context starts to matter.
- Workflow routing and backtracking: If a strategy isn’t producing useful results, the orchestrator reevaluates and redirects its approach, keeping a history of what has worked and what hasn’t.
- Sub-agent compilation: It dynamically builds execution graphs for specialized agents, then invokes them in parallel when appropriate.
- Decision making: Instead of rigid logic trees, it uses conversational reasoning to decide which paths to explore next.
- Historical learning: Historical test results against the target are used to generate a warm start global adaptive vector.
- Global adaptive vector: During execution, a global learning algorithm identifies success and refusal patterns and shares them across parallel test executions.
Once the orchestrator defines a strategy, execution is handed off to sub-agents that are optimized for different styles of attack. Each of these specialist agents handles different functions.
SingleTurnAgent
Like its name suggests, the SingleTurnAgent is designed for a single, direct, high-signal interaction.
Its architecture is a simple linear flow:
Generator → Executor → Evaluator
This linear flow makes it fast and effective to perform baseline vulnerability checks, rapid attack iterations, and tool manipulations with a single prompt.
MultiTurnAgent
Most vulnerabilities can’t be found in a single exchange. Instead, they emerge over time, through context building, misdirection, or subtle escalation. This is where the MultiTurnAgent comes in.
The MultiTurnAgent’s architecture adds an internal loop:
Generator → Executor → Evaluator → Controller
This allows it to adapt mid-conversation, refining its approach based on how the system responds.
The MultiTurnAgent performs two types of attacks – scripted and dynamic.
Scripted uses a predefined attack strategy like:
- BadLikert
- TokenOverload
- HarmlessPreconditioning
Dynamic uses LLM-generated conversational flows that evolve in real time, including adaptive system prompts and shifting tactics.
The orchestrator can run the SingleTurnAgent and the MultiTurnAgent in parallel, essentially testing both immediate and long-form weaknesses at the same time.
EvaluatorAgent
The OrchestratorAgent dynamically generates an EvaluatorAgent for each test definition in the run. For example, when you run multiple OWASP test definitions at once, the OrchestratorAgent generates an evaluator for each test definition based on the reasoned LLM interpretation. Results of all testing are automatically correlated and sent back to the platform.
Shared Architecture Components
Underneath the agents is a shared layer that keeps everything consistent, reusable, and scalable. This includes the following:
- ATLASSharedServices: Centralized management for tools, generators, evaluators, and conversation state; this avoids duplication and ensures every agent is operating from the same resource pool.
- ATLASConfig: A unified configuration system with validation and dynamic settings, allowing the entire architecture to be tuned without rewriting core logic.
Supported Frameworks
TrojAI Detect agent-led AI red teaming allows you to select an existing security framework to map test results to, including OWASP, MITRE or NIST. Furthermore, for even greater flexibility, users can create their own test definitions in lieu of using a pre-defined framework.
Benefits of Agent-led AI Red Teaming
Agent-led AI red teaming transforms AI security testing from a complex, multi-step process into a streamlined, intelligent assessment aligned to industry-standard frameworks.
TrojAI Detect’s AI-led red teaming solution makes it easy to perform complex red teaming on AI agents, applications, and models. In just a few quick clicks, you can achieve full-coverage testing. Furthermore, TrojAI Detect automatically maps results to standard security frameworks, making compliance simple.
AI is evolving rapidly. TrojAI allows you to keep up with the blistering pace of change to help you meaningfully reduce real risk.
FAQs
No. Agent-led AI red teaming uses coordinated autonomous agents to run multi-turn, adaptive attacks against AI models, applications, and agents before deployment, not single prompts. The agents retain memory across attacks, correlate results into one report, and map findings to frameworks like OWASP, MITRE, and NIST, surfacing structural weaknesses that simple prompt tests leave buried.
Yes. An orchestrator agent interprets each test definition, selects a strategy based on context, and routes execution to specialized agents. Single-turn agents run fast baseline checks, while multi-turn agents adapt mid-conversation to expose deeper flaws, and an evaluator agent scores each result. All findings are then correlated and mapped to OWASP, MITRE, and NIST.
No. Manual, script-based red teaming tests one behavior at a time and can’t match the speed or scale of autonomous AI, where attacks unfold through multi-turn conversations. Agent-led red teaming runs many adaptive attacks in parallel, retains memory across cycles to evolve its strategies, and surfaces complex risks that static scripts and human testers leave buried.
Yes. TrojAI Detect automatically maps every test result to OWASP, MITRE, and NIST, so security teams can turn raw vulnerabilities into governance, compliance, and risk priorities instead of wading through unstructured data. Teams can also create their own custom test definitions in place of a predefined framework, giving them flexibility while keeping findings aligned to recognized standards.
Yes. A multi-turn attack unfolds across a conversation rather than one prompt, building context, using misdirection, and escalating gradually so small cracks compound into cascading failures. Most serious vulnerabilities never appear in a single exchange, which is why multi-turn testing matters: it mirrors how real-world threats like prompt injection actually behave against production AI systems.
Yes. Agent-led AI red teaming tests AI models, applications, and autonomous agents. Because the testing agents reason and adapt rather than follow fixed scripts, they can probe agent-to-agent interactions, tool calls, and multi-step workflows where vulnerabilities often hide. This matters as enterprises move from single chatbots to agentic systems, where one weak link can trigger cascading failures across connected agents.
