Model Red Teaming
Use adversarial prompts, jailbreak sets and multi-turn attacks to test boundaries.
Model Security Assessment
Red-team models, APIs, plugins and supply chains for jailbreaks, backdoors, model theft, resource abuse and dependency risk, with verifiable remediation.
Core Risks to Address
Models exhibit jailbreak, prompt leakage, backdoor or unsafe-output behavior
Inference APIs face extraction, membership inference and resource exhaustion
Training data, model files, plugins and open-source dependencies lack provenance
No reproducible security baseline or remediation verification exists before launch
Core Capabilities
API & Asset Review
Assess auth, rate limits, error leakage, model files and exposed inference services.
Supply-Chain Review
Evaluate model provenance, data dependencies, plugins and third-party services.
Baseline & Retest
Deliver risk ratings, hardening guidance and post-remediation verification.
Typical Scenarios
- Pre-launch validation for in-house or fine-tuned models
- Third-party model, API and intelligent-application procurement
- Model platform upgrades and major-change retesting
- Focused assessment for finance, government and healthcare
Delivery Scope
- Model security test plan and adversarial sample set
- Model, API and supply-chain assessment report
- Risk ratings, hardening guidance and remediation backlog
- Retest report and launch security baseline
Delivery process
Define scope
Map models, APIs, dependencies and use cases.
Design tests
Build risk tests around business objectives.
Run assessments
Record conditions, evidence and reproduction steps.
Improve and retest
Apply recommendations and verify key findings.
Deliverables
Scope, schedule and acceptance criteria are agreed following assessment.
Discuss this approachDeliverables to scope together
- Scenario and risk-test records
- Integration, policy configuration and validation notes
- Operational audit and improvement actions
Explore further
LLM Application Security
A unified security control point for prompt-injection defense, input/output inspection, sensitive-data protection, access control and model-call audit.
RAG security
Protect retrieval and generation against unauthorized access, knowledge poisoning, sensitive-fragment leakage and ungrounded responses.
AI Security
Lifecycle security for LLM, AIGC, RAG and agent applications across data, model, application and operations layers.