AI Security

AI security testing for LLM applications, RAG systems and AI agents

AI systems create new attack surfaces. QSEIDON tests AI applications, LLM-based systems, RAG architectures and agentic workflows against new threats such as prompt injection, data leaks, insecure tool use and abuse scenarios.

Classic penetration testing tests applications. AI security testing tests behavior, context and abuse potential.

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What we test

Prompt Injection

Direct and indirect prompt injection attacks that manipulate model behavior or bypass instructions.

Jailbreaks and Policy Bypass

Techniques for bypassing safety filters, system prompts and intended model behavior.

RAG Data Leakage

Extraction of sensitive content from retrieval-augmented generation knowledge bases.

Insecure Tool Use

Abuse of tools and function calls exposed to AI agents in agentic workflows.

Agentic Workflow Abuse

Multi-stage attack scenarios against autonomous AI agents with extended capabilities.

Authorization Flaws

Flawed access controls in AI applications that enable unauthorized data access or actions.

Sensitive Information Exposure

Extraction of training data, system prompts, user data or internal configuration details.

Misuse Scenarios

Repurposing AI system capabilities beyond their intended use case.

Typical Targets

Chatbots and conversational AI
Copilots and AI assistants
RAG systems
AI agents with tool access
Internal AI tools
Cloud-based AI workflows

Our Approach

01
Architecture Review
Understand AI system design, data flows, integrations and trust boundaries.
02
Threat Modeling
Identify AI-specific attack vectors and abuse scenarios for the system.
03
Adversarial Testing
Manual testing against prompt injection, jailbreaks, data extraction and tool abuse.
04
Misuse Analysis
Explore abuse scenarios beyond the intended system purpose.
05
Reporting
Deliver findings with attack scenarios, risk prioritization and remediation recommendations.

Deliverables

AI security findings and attack scenarios
Misuse and abuse case documentation
Risk prioritization
Recommended safeguards and mitigations
Recommendations for secure architecture