Test AI systems beforethey fail in production.
Red-team LLM apps, RAG systems, and AI agents for prompt injection, jailbreaks, unsafe tool use, and sensitive data leakage before release.
Assessment Console
staging-agent-gateway / release-candidate
Attack scenarios
Queued + livePrompt injection via retrieved policy
FlaggedRole-play jailbreak escalation
FlaggedUnsafe tool-call after context override
BlockedSensitive system prompt leakage
ObservedEvidence and traces
7 findings capturedFinding
Instruction override reached tool planner
A retrieved document injected an alternate system priority, causing the agent to justify a risky external action without a valid approval step.
Prompt path
User input → retrieval chunk → planner
Unsafe behavior
Tool routing changed after hostile context merge
Recommendation
Isolate retrieval instructions and re-check tool policy
Retrieved document attempted instruction override
criticalAgent selected external tool with manipulated justification
elevatedHidden policy string surfaced in partial completion
criticalCurrent risk
Jailbreak and prompt-injection exposure score
Report package
Built for serious AI review
Honest trust signals for teams that care how the testing actually works.
Designed for modern LLM product teams
Security review built for teams shipping assistants, copilots, and agent workflows into real customer paths.
Built for prompt injection and agent-risk testing
Focus on instruction hierarchy, unsafe actions, tool abuse, and failure modes standard QA rarely catches.
Structured for repeatable security review
Move from one-off adversarial testing to evidence-backed regression checks before release.
Local-first and API-compatible workflows
Useful for teams validating staging systems, internal sandboxes, local models, or hosted endpoints.
The Threat Surface
LLM products fail in ways normal QA does not model.
Prompt injection, jailbreaking, unsafe tool execution, retrieval poisoning, and workflow drift create new risk paths. TESTOS is built to test the behavior layer where those failures happen.
Prompt injection
Hidden or indirect instructions override intended behavior through user input, retrieved content, or tool responses.
Jailbreaks
Safety controls degrade under adversarial phrasing, role-play, multi-turn steering, and obfuscated prompts.
Unsafe tool use
Agents can overreach permissions, take unintended actions, or execute on manipulated context without sufficient checks.
Sensitive data leakage
System prompts, secrets, internal policies, or retrieved proprietary content surface in responses when boundaries break.
Poisoned retrieval
Untrusted documents reshape answers, citations, and downstream actions inside RAG pipelines.
Workflow failure
Multi-step agents drift, recurse, skip approvals, or mis-handle escalation logic under adversarial conditions.
Platform
A security testing layer for modern AI systems.
TESTOS is positioned as a practical platform for adversarial evaluation, evidence capture, and release-gate re-testing across LLM applications, RAG workflows, and agents.
Attack library for AI systems
Run focused suites for prompt injection, jailbreaks, data exposure, tool misuse, and agent failure modes.
Repeatable red-team scenarios
Model risky user journeys, multi-turn manipulation, and adversarial document flows with structured scenarios.
Prompt injection and jailbreak evaluation
Stress the exact instruction hierarchy and refusal behavior your product depends on in production.
RAG and agent workflow testing
Validate retrieval quality, context boundaries, tool routing, side effects, and approval controls together.
Evidence capture and reporting
Keep failing prompts, traces, risk summaries, and findings organized for engineering and security review.
Release-gate regression testing
Re-test fixes on the same scenarios before release so security learning compounds instead of resetting.
How It Works
A clean path from adversarial testing to release confidence.
Keep the workflow simple: connect the surface you are shipping, run targeted attack suites, inspect the evidence, and re-test fixes before release.
Connect your AI surface
Point TESTOS at a staging endpoint, model gateway, RAG workflow, or agent runtime without rebuilding your product around the tool.
Run targeted attack suites
Execute tests for prompt injection, jailbreaks, risky retrieval behavior, sensitive data exposure, and unsafe tool execution.
Review findings and evidence
Inspect severity, attack traces, failing prompts, tool events, and report-ready evidence that engineering teams can act on.
Re-test before release
Turn key failures into repeatable checks so fixes are validated before deployment, not after an incident.
Use Cases
Security validation for the AI products teams are actually shipping.
From RAG assistants to tool-using agents, TESTOS fits the common release patterns where adversarial testing matters most.
RAG chatbot review
Stress untrusted documents, citation quality, retrieval boundaries, and hidden instruction injection inside knowledge workflows.
Customer support assistant testing
Validate refusal behavior, escalation controls, tool calls, and leakage risk in customer-facing support paths.
Internal copilot validation
Reduce exposure of secrets, internal policies, and sensitive context across enterprise or developer copilots.
AI agent workflow hardening
Test tool permissions, approval steps, retries, side effects, and instruction integrity across long-running agent flows.
Release-gate security review
Add structured adversarial evaluation before shipping new LLM features, not as a retroactive incident response measure.
Ready to ship
Ship AI with fewer blind spots.
Red-team LLM apps, RAG systems, and AI agents before users discover the weakness for you.