Six coordinated experts.
One autonomous swarm.
Each expert is a fine-tuned attack model with a specialty. A bandit orchestrator routes traffic to the expert most likely to breach — and keeps learning which attackers work best against your defenses.
Prompt Injection
Override system instructions through direct and indirect injection — including system-prompt extraction and instruction hijacking across multi-turn conversations.
Jailbreak
Bypass safety guardrails with DAN-style prompts, role-play exploits, and unrestricted-mode triggers tuned to the target model family.
Exfiltration
Extract protected data via SQL-injection-through-LLM, secret exfiltration, and PII leakage. 289 verified exploits on multi-agent targets.
289 verified exploits
Tool Abuse
Misuse agent tools: command injection, SSRF, path traversal, metadata extraction, and tool-call hijacking in MCP and LangChain agents.
RAG Poisoning
Exploit document retrieval through semantic query manipulation, knowledge-base poisoning, and embedding-space attacks on vector stores.
Memory Injection
13 attack families including false conversation history, temporal triggers, cross-session propagation, tool-description poisoning, and multi-agent function-call attacks.
687 verified exploits · 13 families
976 verified exploits across 6 coordinated experts in one autonomous campaign.
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.
A swarm that learns your defenses
faster than you can ship them.
Built for engineers who want to see the internals. LoRA-fine-tuned experts, a bandit orchestrator, and a reinforcement-learning loop that makes every campaign smarter than the last.
Phase 01
Recon
Phase 02
Initial Breach
Phase 03
Escalation
Phase 04
Exploitation
Phase 05
Persistence
Every run plays out a full attack.
Recon, initial breach, escalation, exploitation, persistence — the same shape as a real adversary. Each phase runs concurrent attack batches on a time budget you set.
Six attackers compete in real time.
Each expert has a specialty. The swarm learns which ones are landing against your target and routes more traffic to them as the campaign runs.
Every campaign trains the next one.
Between runs, the experts retrain on what worked and what didn't. The same swarm, three training generations later, breaches targets 3.5× more often.
Tuned to how hard your target is.
From undefended endpoints to agents running input filters and content guardrails, the swarm adjusts its approach. Reports include the path that got past — not just the finding.
Numbers from the lab,
not the marketing deck.
Every exploit is reproducible. No synthetic benchmarks, no cherry-picked wins.
976
verified exploits
Every one reproducible — validated against multi-agent orchestrators and LangChain agents.
60%
attack success rate
On memory-poisoning attacks — the hardest class of agent exploits, and the one most scanners don't test at all.
52.5%
break rate on hardened systems
Even against multi-agent stacks shipping with input filtering and content guardrails enabled.
+250%
lift from self-improvement
Template attacks succeed 21% of the time. Our RL-trained experts succeed 74%.
How findings are verified: We attack your system the way a real adversary would — sending inputs, observing outputs. A finding only counts as a breach when we watch it happen: sensitive content in a response, an unauthorized tool call, a backdoor instruction landing in memory, or data being exfiltrated. Every breach ships with the exact prompt, response, and detection signal, so your team can replay it end-to-end.