AI & LLM / AI Agent Penetration Testing

Direct and indirect prompt injection attacks that manipulate LLM behavior, bypass guardrails, and execute unauthorized actions through crafted inputs.

Extracting training data, PII, API keys, proprietary information, and system prompts from LLM responses through carefully crafted queries.

Compromised training data, poisoned pre-trained models, vulnerable plugins/extensions, and malicious third-party model dependencies.

Manipulating training data or fine-tuning processes to inject backdoors, bias outputs, or degrade model performance in targeted scenarios.

LLM outputs passed to downstream systems without sanitization, leading to XSS, SSRF, code execution, or privilege escalation in connected apps.

AI agents with excessive permissions, uncontrolled tool use, autonomous actions without human-in-the-loop, and privilege escalation through tool chaining.

Extracting system prompts, hidden instructions, and configuration details that reveal business logic, security controls, or sensitive operational data.

Exploiting RAG pipelines, poisoning vector databases, manipulating embedding spaces, and injecting malicious content into retrieval contexts.

Hallucination exploitation, generating convincing but false information, deepfake-assisted social engineering, and trustworthiness manipulation.

Denial-of-service through resource exhaustion, excessive token consumption, recursive prompt loops, and wallet-draining attacks on pay-per-token APIs.