Secure Next-Gen AI Systems & LLMs
Protect artificial intelligence applications, autonomous agents, RAG pipelines, and vector infrastructure against adversarial manipulation and emerging cyber threats.
AI Security Assessment Services
Our offensive security specialists simulate real-world adversarial attacks across every tier of your AI tech stack.
LLM Security Assessment
Comprehensive adversarial security testing of foundation models and fine-tuned application wrappers against standard risk taxonomy.
Prompt Injection & Jailbreaking
Direct and indirect prompt injection simulation to test system prompt resistance, context window isolation, and guardrail boundaries.
RAG & Vector DB Security
Reviewing data retrieval pipelines, vector index permissions, knowledge base poisoning vulnerabilities, and embeddings access controls.
AI Agent Security & Tool Misuse
Offensive testing of autonomous AI agents with function-calling capabilities to prevent unauthorized API actions and lateral privilege escalation.
Sensitive Data & PII Leakage
Evaluating training data extraction vectors, system prompt extraction, and unintended exposure of customer PII through model outputs.
AI Supply Chain & Model Safety
Auditing third-party model dependencies, Hugging Face artifact integrity, unverified Python libraries, and unsafe model deserialization.
The AI Threat Landscape
Modern AI implementations introduce novel attack surfaces that conventional Web Application Firewalls (WAFs) cannot detect.
Prompt Injection (Direct / Indirect)
Untrusted inputs forcing the model to bypass safety guardrails or execute unauthorized actions.
Sensitive Information Disclosure
Unintended leakage of confidential system prompts, user context, or proprietary database schema.
Insecure Plugin & Tool Execution
Autonomous agents calling external tools or running code execution without context validation.
RAG Data Poisoning
Malicious content injected into knowledge stores, altering model outputs and downstream search results.
Model Denial of Service (DoS)
Exhausting model context windows or GPU compute resources via intentionally recursive input sequences.
Excessive Agency
Granting foundation models excessive permissions, broad API keys, or unrestricted write access.