Agent Red Team
Most red-teaming tools test text output: toxicity, jailbreaks, hallucination. That misses the real threat. When an agent has tools, memory, and approval authority, the failure mode is unauthorized action. ART maps your attack surface across 12 threat categories, simulates adversarial scenarios against tools, memory, identity, and approval logic, and returns deterministic findings with hardening drafts. Validation is code, not LLM.
Why now: The EU AI Act requires adversarial robustness testing for high-risk AI systems. NIST AI RMF mandates TEVV for deployed AI. Every company shipping AI agents with tool access needs pre-deployment adversarial testing. The compliance window is closing.
Target agent
fintech-assistant-v2Analysis will begin when visible
Free adversarial scan
50 more characters needed
50KB max
Supports system prompts, skill files (.md), tool descriptions, and policies (.txt)
How it works
AI agents are being deployed into production without adversarial testing. The failure mode is not a hallucinated sentence. It is a fraudulent transaction, a leaked database, a bypassed approval chain, or a poisoned decision that compounds across sessions. ART catches these failures before deployment.
01 / Map
Inventory every prompt, tool schema, SKILL.md, MCP config, memory store, retrieval source, approval gate, identity, and trust boundary your agent depends on.
02 / Attack
Run 10 attack packs across 30 cases: prompt injection, poisoned memory, tool misuse, identity spoofing, approval bypass, data exfiltration, cross-agent trust abuse, and supply chain exploitation.
03 / Harden
Receive severity-ranked findings with full exploit traces, evidence chains, and implementation-ready hardening drafts for prompts, schemas, policies, and action controls.
Why this matters now: The attack surface for agentic AI is fundamentally different from traditional software. A hostile webpage, a poisoned memory entry, an unsafe MCP server, or a malicious plugin can turn a helpful agent into one that exfiltrates data, executes unauthorized actions, and bypasses every approval gate, while the interface still looks completely normal to the operator.
The problem
Most red-teaming tools evaluate text output quality: toxicity, bias, hallucination. That misses the actual threat. When an AI agent has access to tools, memory, and external systems, the failure mode is unauthorized action: executing trades without approval, exfiltrating user data through tool calls, escalating privileges via identity confusion, or poisoning its own memory to corrupt future decisions. ART is built specifically to find these behavioral failures.
Agents can be manipulated into calling tools with dangerous arguments, executing forbidden operations, or chaining actions to bypass safeguards.
False context injected into memory or RAG persists across sessions and silently shifts downstream tool calls and decisions.
Attackers inherit admin privileges, impersonate trusted users, and move through approval workflows intended for different roles.
Third-party components carry hidden instructions, unsafe tool definitions, or execution logic that activates inside your agent.
12 threat categories tested
Prompt injection
Direct and indirect instruction hijacking through user messages, retrieved documents, web content, and multi-turn conversation setups.
Tool misuse and unsafe execution
Forbidden tool calls, dangerous arguments, excessive agency, unauthorized code execution, and action sequences that bypass intended constraints.
Data exfiltration
Sensitive data extracted through tool outputs, crafted responses, side channels, and unauthorized database queries.
Memory and RAG poisoning
Persistent false context injected into vector stores, conversation memory, or retrieval pipelines that corrupts future decisions.
Identity and approval bypass
Confused-deputy attacks, privilege escalation via role confusion, approval drift, and impersonation of trusted identities.
Multi-agent and supply chain
Cross-agent trust exploitation, hostile MCP servers, malicious plugins, and upstream prompt contamination through third-party components.
Standards alignment
Every ART finding maps to a documented attack class from published AI security frameworks. This is not a proprietary risk taxonomy. It is a structured testing layer built on the same standards that NIST, OWASP, and MITRE use to define adversarial threats to AI systems.
ART tests for prompt injection (LLM01), sensitive data disclosure (LLM02), supply chain risks (LLM03), data poisoning (LLM04), improper output handling (LLM05), excessive agency (LLM06), system prompt leakage (LLM07), and unbounded consumption (LLM10). Not covered: vector/embedding weaknesses (LLM08) and misinformation (LLM09), which address model output quality rather than agent action safety.
NIST AI 600-1 identifies prompt injection, training data poisoning, and evasion as top generative AI risks. NIST AI RMF mandates Test, Evaluation, Verification and Validation (TEVV) for AI systems. ART operationalizes TEVV specifically for agentic architectures: testing whether agents can be made to take unauthorized actions through adversarial inputs.
MITRE ATLAS maps adversarial tactics against ML systems. ART extends this to agentic architectures: tool-call manipulation, memory poisoning, identity escalation, and multi-agent trust abuse are active attack surfaces for deployed agents that existing ATLAS tactics do not yet fully cover.
The gap ART fills: OWASP, NIST, and MITRE define what can go wrong with AI systems. None of them ship a testing tool. Compliance frameworks mandate "adversarial robustness evaluation" without providing implementation. ART is the implementation layer. Paste your agent config, get a structured adversarial report with exploit traces and hardening drafts that map directly back to these frameworks.
The report
Every ART report passes through 22 deterministic validation rules written in code, not LLM judgment. Findings that fail validation are rejected. No hallucinated vulnerabilities. No inflated severity. Every claim is evidence-backed, traceable, and mapped to a specific threat category.
System summary
What your agent is, what tools it controls, and what trust boundaries exist between components.
Severity-ranked findings
Each finding includes exploit chains, evidence anchors, confidence scores, and the specific threat category it maps to.
Exploit traces
Step-by-step attack paths showing exactly how each vulnerability can be triggered and what damage results.
Hardening drafts
Implementation-ready mitigations with enforcement layer, scope, complexity estimate, and deployment guidance.
Architecture inferences
What the analysis inferred about your agent capabilities, trust model, and implicit assumptions.
Residual risk
What remains after mitigations are applied. No false reassurance. Honest about what is not covered.
Coverage grid
12 threat categories mapped with explicit coverage depth for each. Shows what was tested and what was not.
Validation metadata
22 deterministic rules. Pass count, retry count, validation status. Full audit trail of the analysis itself.
Example finding
Unrestricted tool invocation via indirect prompt injection
A crafted user message can instruct the agent to call execute_trade or send_email without authorization checks, bypassing the intended approval flow.
Exploit path
Recommended fix
Add explicit tool-level approval gates for execute_trade and send_email. Require user confirmation before any financial or communication action. Enforce at the tool schema layer, not the prompt.
Pricing
Adversarial
1 full adversarial scan
Builder
30 scans/month for active builders
Team
150 scans/month for teams
22
Deterministic validation rules (code, not LLM)
12
Threat categories mapped to OWASP / NIST / MITRE
0
Hallucinated findings accepted