Agent and tool abuse
Tests whether agents can be steered into unsafe tool calls, privilege escalation, data access outside policy, or multi-step workflows that bypass approval gates.
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Automated Red-Teaming
Hundreds of adversarial simulations against your agents, discovering attacks that traditional testing can't reach. Mapped to OWASP, exportable to your SOC, replayable against any version.
Finance support regression · v14 against guardrail candidate
finance-agent · guardrail candidate v14
We simulate real-world attackers 24/7 across your agents, tools, and integrations—so you always know where you stand.
Our AI attackers use evolving tactics to find logic flaws, tool abuse, privilege escalations, and data exposure—at scale.
We don’t just find issues—we quantify risk, show impact, and re-test continuously so you can close gaps with confidence.
Built for adversaries
# Launch a red-team campaign with one command: # $ ga redteam launch -f campaign.yaml target: finance-agent algorithm: tap behaviors: - LLM01_prompt_injection - LLM02_data_disclosure - LLM06_excessive_agency - LLM07_sysprompt_leak config: runs: 425 workers: 4 depth: 4 export: siem,owasp
What gets tested
The platform tests the deployed AI system, not just the base model. Campaigns reason across prompts, tools, memory, retrieval, identity, and business logic so findings match the failures attackers would actually exploit.
Tests whether agents can be steered into unsafe tool calls, privilege escalation, data access outside policy, or multi-step workflows that bypass approval gates.
Runs direct, indirect, multi-turn, and retrieval-borne attacks against prompts, memories, documents, and connected knowledge bases.
Looks for sensitive prompt leakage, cross-tenant disclosure, unsafe citations, hidden context exposure, and exfiltration through connected tools.
Replays confirmed exploits against new model versions, prompt changes, tool updates, and policy changes before they reach production.
Ingest agents, prompts, tools, permissions, retrieval sources, policies, and business-critical actions so campaigns target the real attack surface.
Use automated attack strategies such as TAP, PAIR, Crescendo, encoding variants, and multi-turn social engineering to search for exploitable behavior.
Cluster duplicate attempts, assign severity, attach traces, and separate harmless policy friction from issues that create operational risk.
Turn each confirmed exploit into a regression test that runs against future deployments and feeds runtime controls when a guardrail is needed.