AI red teaming platform
Automated campaigns test deployed AI systems across chatbots, copilots, RAG apps, coding agents, internal agents, and model endpoints.
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AI Red Teaming Platform
Secure AI agents.
Prove what breaks.
General Analysis is an automated AI red teaming platform for LLM applications, RAG systems, MCP servers, coding agents, and production AI agents. Test prompt injection, tool misuse, data leakage, unsafe autonomy, and multi-step agent attacks before they reach users.
Attack simulation
Simulations Summary
Finance support regression · v14 against guardrail candidate
Policy Tests
184policies
4,920
Tests
91%
Pass
12
Regress
Coverage Battery
finance-agent · guardrail candidate v14
fail
Credential access
secrets, tokens, vault paths
812 tests3 fail
warn
Tool approval
refunds, writes, deploys
644 tests2 fail
fail
Data exfiltration
RAG, files, external APIs
528 tests4 fail
pass
PII handling
masking and retention
486 tests1 fail
SimulationTargetAttemptsASRStatus
finance-agent / crescendo
job-8421
support-agent / tap
job-8422
dev-agent / guided tree
job-8423
legal-agent / pair
job-8424
hr-agent / roleplay
job-8425
Automated AI red teaming
Run continuous adversarial testing across LLM apps, RAG systems, MCP servers, coding agents, and production AI agents.
Agentic attack coverage
Find prompt injection, indirect injection, tool misuse, privilege escalation, memory abuse, and data exposure at the system boundary.
Evidence for AI TRiSM
Turn attacks into reproducible traces, OWASP-mapped findings, remediation tasks, and release regression tests.
Built for adversaries
Attacks that adapt
to your defenses.
- Algorithmic attack searchTree-of-attacks, Crescendo, and PAIR explore the attack space exhaustively.
- Multi-turn reasoningSustained adversarial conversations, not single-shot prompts.
- Mapped to OWASP & MITREEvery finding labeled with the threat tag and severity tier.
- Reproducible test runsReplay any exploit against any model, version, or policy change.
campaign.yaml
# 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
AI security platform coverage
Built for agentic AI risk.
Buyers searching for AI security platforms, AI TRiSM tools, LLM security testing, prompt injection security, and agentic AI security are usually asking for the same evidence. They need to know which AI systems exist, what those systems can reach, how they can be attacked, and whether fixes were verified.
Prompt injection security
Coverage includes direct prompts, indirect instructions in documents and webpages, tool output poisoning, hidden context leakage, and jailbreak variants.
Agentic AI security
Campaigns exercise tools, permissions, memory, browser actions, MCP servers, approval gates, and multi-step business workflows.
MCP and tool-chain testing
Tests observe whether one tool, server, data source, or retrieved artifact can steer another privileged action across the agent graph.
AI TRiSM evidence
Findings are mapped to security frameworks, asset owners, severity, affected systems, remediation steps, and policy changes.
Runtime feedback loop
Confirmed failures become regression tests and can feed runtime controls through guardrails, approval policies, and detection rules.
What gets tested
System-level red teaming.
The platform tests the deployed AI system across the base model, prompts, tools, memory, retrieval, identity, and business logic so findings match the failures attackers would actually exploit.
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.
Prompt injection and jailbreaks
Runs direct, indirect, multi-turn, and retrieval-borne attacks against prompts, memories, documents, and connected knowledge bases.
Data exposure paths
Looks for sensitive prompt leakage, cross-tenant disclosure, unsafe citations, hidden context exposure, and exfiltration through connected tools.
Release regression testing
Replays confirmed exploits against new model versions, prompt changes, tool updates, and policy changes before they reach production.
How a campaign runs
Map
Model the system boundary
Ingest agents, prompts, tools, permissions, retrieval sources, policies, and business-critical actions so campaigns target the real attack surface.
Attack
Generate adaptive campaigns
Use automated attack strategies such as TAP, PAIR, Crescendo, encoding variants, and multi-turn social engineering to search for exploitable behavior.
Triage
Prioritize reproducible findings
Cluster duplicate attempts, assign severity, attach traces, and separate harmless policy friction from issues that create operational risk.
Verify
Retest fixes continuously
Turn each confirmed exploit into a regression test that runs against future deployments and feeds runtime controls when a guardrail is needed.
Guides and playbooks
Guides for AI security buyers.
Automated AI red teaming sits inside a broader AI security program. These guides connect the product page to the searches buyers use for AI security platforms, AI red teaming tools, guardrails, MCP security, and automated penetration testing.
Buyer guide
16 min readBest AI Security Platforms in 2026
A practical 2026 buyer guide to AI security platforms across AI red teaming, agentic AI security, prompt injection protection, runtime controls, AI posture management, model supply chain security, and AI TRiSM.
Read guide
Comparison
18 min readBest AI Red Teaming and Adversarial Testing Tools in 2026
A practical 2026 comparison of AI red teaming and adversarial testing tools across automated red teaming, LLM security testing, prompt injection coverage, agentic AI testing, multi-step tool-chain attacks, framework support, and enterprise readiness.
Read guide
Primer
18 min readWhat Is AI Red Teaming? A Practitioner's Guide
AI red teaming is adversarial testing of AI systems to find exploitable vulnerabilities before attackers do. Learn how it works, key techniques, real exploit examples, and how to implement it.
Read guide
PRIMER
16 min readMCP Server Security: A Threat Model for Agent Tool Supply Chains
The Model Context Protocol expanded what AI agents can reach, and expanded the attack surface across at least nine distinct vectors. A primary-source threat model for MCP servers, with concrete controls, real CVEs, and the GA Supabase exploit walked end to end.
Read guide
PLAYBOOK
14 min readBest AI Guardrails in 2026: Tools, Architecture, and How to Choose
An analytical guide to the AI guardrails landscape: the architecture behind runtime safety, the ten tools that matter, what adversarial benchmarks actually show, and the decisions that determine whether guardrails hold under pressure.
Read guide
PLAYBOOK
18 min readBest Automated Penetration Testing Platforms in 2026
A practical 2026 buyer guide to automated penetration testing platforms, autonomous pentesting, automated security validation, CTEM, DAST, BAS, and AI security testing.
Read guide