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AI red teaming

Continuous AI red teaming
for LLMs and agents.

ProofLayer runs autonomous attack campaigns against LLM applications, multi-agent systems, RAG pipelines, and MCP servers. Every verified breach includes a replay trace and audit-ready evidence.

Talk to usSee attack coverage
Prompt injection Tool abuse Data exfiltration RAG poisoning Memory poisoning MCP attacks
Attack

Test the attack classes
your scanners miss.

Autonomous campaigns test prompt injection, jailbreaks, data exfiltration, tool abuse, RAG poisoning, and memory injection.

Prompt Injection

Override system instructions through direct and indirect injection — including system-prompt extraction and instruction hijacking across multi-turn conversations.

OWASP LLM01MITRE AML.T0051

Jailbreak

Bypass safety guardrails with DAN-style prompts, role-play exploits, and unrestricted-mode triggers tuned to the target model family.

OWASP LLM07

Exfiltration

Extract protected data via SQL-injection-through-LLM, secret exfiltration, and PII leakage. 289 verified exploits on multi-agent targets.

OWASP LLM06MITRE AML.T0024

289 verified exploits

Tool Abuse

Misuse agent tools: command injection, SSRF, path traversal, metadata extraction, and tool-call hijacking in MCP and LangChain agents.

OWASP LLM08

RAG Poisoning

Exploit document retrieval through semantic query manipulation, knowledge-base poisoning, and embedding-space attacks on vector stores.

OWASP LLM03

Memory Injection

13 attack families including false conversation history, temporal triggers, cross-session propagation, tool-description poisoning, and multi-agent function-call attacks.

OWASP LLM04MITRE AML.T0054

687 verified exploits · 13 families

976 verified exploits across 6 coordinated experts in one autonomous campaign.

Target Coverage

If you deployed it,
we can red-team it.

Point the swarm at an endpoint, a multi-agent system, or an MCP server. Same campaigns, same reports — regardless of what's behind the adapter.

LLM APIs

OpenAI, Anthropic, Azure OpenAI, self-hosted Qwen / Llama / Mistral.

Multi-Agent Orchestrators

7-agent Opus-style systems, LangGraph, custom orchestration with tool-using agents.

MCP Servers

Native adapter for Model Context Protocol servers — tool discovery, schema fuzzing, poisoning.

ReAct / LangChain Agents

LangChain agents with vector-store memory. Tool-call interception and prompt-context attacks.

RAG Pipelines

ChromaDB and general vector stores — retrieval poisoning, query manipulation, context injection.

Benchmark Targets

AgentDojo and custom red-team targets — for validating attack transferability across architectures.

Portable attacks

Exploits built against one architecture transfer to others — no rewriting, no re-tuning. Build an attack once, port it anywhere.

Multi-agent orchestrators↔LangChain agents↔Benchmark targets
The ProofLayer loop

Attack. Detect. Prove.
Then run it again.

Continuous attacks create current evidence. New models, tools, and agent workflows enter the same loop as you ship them.

ProofLayer continuous security loop: autonomous attacks become verified findings, then audit-ready evidence, and repeat after every release
AgentDojo benchmark

Red-team performance
across real agent workflows.

Attack success and legitimate task utility measured across Workspace, Travel, Banking, and Slack suites.

Benchmark results
Attack success rate Utility

Utility measures legitimate task completion while the attack policy runs.

About AgentDojo

Prove what happens
when your AI is attacked.

Run the attack. Replay the finding. Send the evidence.

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