AI Security & Governance
Generative AI Security
Protect your LLM applications against threats specific to generative AI
The new threats of generative AI
Integrating large language models (LLMs) into applications opens an unprecedented attack surface: prompt injection, data exfiltration, poisoning, guardrail bypass or generation of harmful content.
Traditional security controls do not cover these risks. A specialized approach is required to test and harden systems built on generative AI.
Frameworks applied:
Our assessments rely on the OWASP Top 10 for LLM Applications and the MITRE ATLAS matrix of threats targeting AI systems.
The risks we address
Prompt Injection
Manipulation of the model's instructions to hijack its behavior or guardrails.
Data Leakage
Exfiltration of sensitive information through the context, responses or model memory.
Poisoning
Tampering with training data or RAG sources to corrupt responses.
Denial of Service
Overloading models with costly requests, degrading availability and costs.
Unsafe Outputs
Generation of erroneous, toxic or exploitable content (code, XSS) without control.
Agent Abuse
Hijacking of AI agents with access to sensitive tools, APIs or systems.
Our testing methodology
Scoping
Identification of the use cases, models, data and integrations to assess.
Risk Mapping
Analysis of the attack surface specific to your generative AI applications.
AI Red Teaming
Targeted offensive testing: injections, exfiltration, guardrail bypass.
Analysis & Prioritization
Qualification of vulnerabilities and their real business impact.
Recommendations
Concrete remediation plan: filtering, isolation, guardrails and monitoring.
Use cases
Secure your generative AI
Have the robustness of your LLM applications assessed by our AI offensive security experts.
Request an assessment