LLM & Generative AI Security
Security testing for large language models and GenAI features — aligned to the OWASP Top 10 for LLM Applications.
Overview
GenAI features open an attack surface traditional testing never anticipated: manipulated prompts, leaked system instructions, poisoned context, and agents that can be tricked into acting on an attacker's behalf. We test your LLM applications, RAG pipelines, and AI agents the way a real adversary would — then help you put guardrails where they matter.
What we cover
The ground a typical engagement covers — always tailored to your scope.
Prompt injection, jailbreaks, and system-prompt extraction
Insecure output handling and downstream injection
RAG and vector-store poisoning and data leakage
Agent / tool-use abuse and excessive agency
Sensitive-data disclosure through model responses
Guardrail, rate-limit, and content-filter validation (OWASP LLM Top 10)
What every engagement delivers
Impact-rated findings
Every issue rated by real-world impact and mapped to OWASP & MITRE ATT&CK — not just a raw CVSS score.
Developer-ready fix guidance
Practical, specific remediation steps for each finding, written for the engineers who'll fix it.
Executive summary
A board-readable view of risk and posture alongside the full technical detail.
Free retest
We re-test your fixes and issue a clean report you can hand to customers and regulators.
Related
AI & ML Security
Security for models, prompts, and data pipelines — aligned to the OWASP Top 10 for LLM Applications.
Learn moreAPI Security Testing
REST, GraphQL, gRPC, and SOAP endpoints tested against the OWASP API Security Top 10 — where modern breaches increasingly begin.
Learn moreArchitecture Review
Design-level threat modeling of trust boundaries, authentication flows, and data handling — catching whole classes of risk before a line ships.
Learn moreReady to get started
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