Own your data and inference
Choose sovereign hosting and fully local inference. In that configuration, sensitive workloads, model processing and evidence stay inside your enterprise environment, with no external model calls.
AI Penetration Testing
AI and LLM Penetration Testing
Can a malicious document make your AI agent leak data or take an action it should refuse? We test the prompts, retrieved content, tools and permissions behind that question, then show your team what failed and how to check the fix.
Need a standard first run? View the first-pentest offer: $999 USD for 250 credits. Confirm specialized AI scope before purchase.
Trusted by leading AI and technology companies
Model Context Protocol security
Verify permissions at the tool boundary.
For an MCP-connected application, agree which identities, resources and actions the agent may use. Then evaluate the boundary between a model's suggestion and an action the application actually authorizes. Use test accounts and synthetic resources, with approval and stop conditions agreed before execution.
- Token audience validation: establish which server a credential is intended for.
- Tool and object authorization: check access across roles and tenant boundaries.
- Approval-to-action binding: confirm that the approved action is the action executed.
See the MCP security guide for the wider threat model, or discuss your AI assessment scope. Specialized AI testing must be scoped before purchasing a standard black-box run.
LLM assessment methodology
What happens in an AI application assessment?
Start with the deployed application and the actions it permits. Agree the model, retrieval, tools and data boundaries before choosing test cases.
1. Inventory
Application, model and deployment versions; retrieval stores; MCP servers; tools; external processors; system and data owners.
2. Trust boundaries
Caller identities, roles, tenant boundaries, data classes, tool permissions and approval gates.
3. Authorized cases
Approved synthetic inputs, expected allow/deny decisions, permitted read and write effects, test budget, monitoring and stop conditions.
4. Evidence
Configuration, sanitized trace, policy decision, data or action reached, expected result and observed result. Record inconclusive cases and stochastic variation.
5. Remediation
Control owner, proposed application or permission change, human approval and deployment reference.
6. Retest
Original boundary case, legitimate control, approved alternate paths, tested version, outcomes and remaining limitations.
Which work needs a separate scope?
- Training infrastructure and training-data access
- Model weights and extraction testing
- Third-party services without written permission
- Production writes, destructive actions and load tests
- Regulatory certification and universal model-safety claims
AI red teaming investigates the behavior and trust boundaries of an AI system. Agentic pentesting uses agents to assess authorized applications and infrastructure. This sequence is a planning framework, with coverage and deliverables agreed in writing; it is not a claim that every behavior or model has been tested.
Coverage
AI security testing, scoped to your system
Depending on the agreed scope, we test AI application surfaces including prompt handling, data pipelines, tools and model deployment. We agree access, permitted actions and exclusions before testing.
Expect scoped findings with sanitized reproduction evidence, the affected trust boundary, impact and remediation guidance. Agree which fixes will be retested and record what remains unverified or outside scope.
Prompt Injection Testing
Systematic testing for direct and indirect prompt injection vulnerabilities that could compromise your LLM application.
Data Pipeline Security
Assessment of training data integrity, RAG implementations, and protection against data poisoning attacks.
Privacy & Data Leakage
Testing for unintended data exposure, PII leakage, and sensitive information disclosure through model outputs.
Model Access Controls
Review of authentication, authorization, rate limiting, and abuse prevention mechanisms.
MCP & Tool Security
Test tool permissions, Model Context Protocol servers, agent trust boundaries, and unsafe side effects.
Output Validation
Analysis of output filtering, content moderation, and protection against harmful or malicious responses.
Compliance Assessment
Review of selected security-framework mappings and assessment evidence. Regulatory opinions and certification require separate work.
Threat Landscape
AI-Specific Attack Vectors We Test
AI systems introduce risks that conventional web security checks may miss. Assessment coverage depends on architecture, threat model, and agreed scope. Testing can include prompt injection, jailbreaks, sensitive data disclosure, retrieval risks, tool misuse, and the surrounding application and infrastructure.
Prompt Injection
Malicious inputs that hijack model behavior
Jailbreaking
Bypassing safety guardrails and content filters
Data Poisoning
Corrupting training data to influence outputs
Model Extraction
Stealing model weights or capabilities
Privacy Attacks
Extracting training data or user information
Supply Chain
Compromised models, libraries, or dependencies
MCP & Tool Abuse
Unsafe actions through agent tools and connected servers
AI red teaming and agentic penetration testing
AI red teaming tests an AI system for risks such as prompt injection, data exposure, and unsafe tool actions. Agentic penetration testing describes using autonomous agents to test applications and infrastructure. The approaches can overlap when agents or AI-enabled services are in scope, but they answer different security questions. Read our agentic penetration testing guide for the platform and buyer-evaluation perspective.
Sources and scope
What supports this page
The assessment sequence and downloadable worksheet are FailSafe editorial planning aids. Examples use synthetic records. Actual models, tools, actions, evidence and exclusions require an agreed scope; the page does not provide regulatory certification or universal model-safety assurance.
By FailSafe Research. Editorial update: .
Questions & answers
Frequently asked questions
Direct answers to common questions about this page.
For an agreed connected engagement, FailSafe takes the workflow from agent deployment and threat modeling through active testing, reproduction of findings, ITSM handoff and agent-generated remediation pull requests within 48 hours of full access and integration readiness. Your team reviews and merges changes and controls production rollout. Confirm the scope and prerequisites with us; the first-pentest promotion separately includes its report window and remediation retesting.
Yes. A fully local FailSafe deployment can keep model inference, source code, prompts, traces and assessment evidence inside your enterprise environment, with no external model calls or cloud fallback in that configuration. We agree hosting, egress restrictions, telemetry, updates and support access as part of deployment design. Regional data storage alone does not provide this control.
FailSafe develops and security-post-trains its GlassBreak cyber models from an open-weights base. Combined with SWARM’s active testing workflow, they support threat modeling, attack-path investigation and reproducible evidence. Enterprise teams can evaluate the model and execution workflow together and choose customer-controlled inference.
It is a scoped security assessment of AI systems such as LLM applications, RAG pipelines, and agents. Testing may cover prompt injection, unsafe tool use, access control, data exposure, and related risks in the surrounding application.
Timing depends on the number of systems, environments, integrations, access provided, and agreed test scope. FailSafe confirms an estimate after scoping the assessment.
The test plan defines approved environments, permitted actions, rate limits, monitoring, and stop conditions before testing. Production testing is performed only when specifically authorized and included in the agreed plan.
Findings include remediation guidance. Retesting can be scoped to verify agreed fixes, with the result recorded in the updated report.
The test plan can reference the OWASP Top 10 for LLM Applications and relevant NIST AI RMF concepts. The selected coverage depends on the system, threat model, and agreed scope. A framework mapping is not a certification or guarantee of compliance.
Ready to Ship Secure AI?
Evaluate your AI application with an authorized assessment. The $999 Startup offer provides 250 credits for one black-box run. Discuss specialized AI testing requirements with us before buying.
View the first-pentest offer after confirming that your AI testing requirements fit its scope.
For enterprise buyers
Make the case for FailSafe.
See how active testing, FailSafe-trained cyber models and sovereign inference work together for your security and engineering teams.
Why choose FailSafe over XBOW?
Compare the complete attack-to-remediation workflow, our cyber models and fully local inference.
Read the guide →FailSafe or Pentera?
See where threat-led application testing, reproducible findings and code remediation fit your program.
Read the guide →FailSafe or Horizon3.ai?
Connect internal testing with application and AI risk, enterprise-owned inference and engineering handoff.
Read the guide →Own your inferencing
Understand how FailSafe can keep model processing, code and evidence inside your enterprise environment.
Read the guide →Why our cyber models matter
Explore the role of security post-training, SWARM execution and evidence in an enterprise assessment.
Read the guide →From demonstrated weakness to reviewed fix
Follow a validated attack path into an ITSM issue, a remediation PR and a retest.
Read the guide →Why FailSafe
Agentic pentesting with control over data and inference.
Start with FailSafe-managed SaaS, or explore sovereign options for government, military and critical infrastructure workloads. Choose where SWARM runs, where evidence is held and where FailSafe’s cyber models process it.
Findings your engineers can reproduce
See how a weakness can be used, what it affects, and what to change. Included evidence helps your engineers reproduce the finding and verify remediation.
AI systems in the test scope
Follow the path from a prompt or retrieved document into tools, identities, APIs, and business actions. Agree the specific surfaces and actions before testing.
FailSafe-trained cyber models
GlassBreak Flash V3 is security-post-trained by FailSafe and paired with SWARM’s active testing. Evaluate the model and execution workflow together on your application.
From finding to remediation PR
Connect reproduced findings to ITSM and let agents open remediation PRs with authorized source access. Your engineers review and merge changes; retest the path after the correction.
Enterprise deployments define the complete inference, egress, telemetry and support boundary. Custom hosting, ITSM and code-remediation integrations are scoped separately from the first-pentest promotion.