Systematic adversarial testing of LLM applications, AI agents, and generative AI systems. 790+ security classes covering prompt injection, jailbreaking, tool misuse, indirect injection, and agent kill-chain attacks.
Explore Nora VeridicAI red teaming is systematic adversarial probing of LLM applications, AI agents, and generative AI systems to discover vulnerabilities before deployment. Unlike traditional pentesting, AI red teaming targets prompt injection, jailbreaking, indirect injection via retrieved content, tool misuse, and multi-step agent kill-chains that have no analogue in classical software security.
Nora Veridic tests direct and indirect prompt injection — instructions hidden in user input, tool outputs, retrieved documents, or function call responses. The testing harness generates thousands of adversarial inputs using 790+ OWASP ASI-aligned security classes, measuring whether injected instructions override the system prompt, exfiltrate data, or trigger unintended tool calls.
AI agents with tool access introduce kill-chain risks: a single compromised tool call can escalate to code execution, data exfiltration, or lateral movement across connected systems. Our testing covers tool misuse, memory poisoning, orchestrator bypass, RLHF evasion, and multi-turn manipulation sequences that compound innocuous steps into high-impact outcomes.
Singapore's PDPA and MAS Technology Risk Management (TRM) guidelines increasingly apply to AI system deployments. AI red team reports provide documented evidence of adversarial testing required by enterprise risk committees and financial services regulators adopting AI governance frameworks.
Automated AI red teaming uses scripted payloads and adversarial fuzzing to scale vulnerability scanning rapidly — best for CI/CD regression testing. Manual red teaming relies on human researchers crafting novel, contextual attacks to uncover sophisticated logical flaws and zero-day jailbreaks. Hybrid combines both: automated broad-spectrum scanning with targeted human analysis for the most comprehensive risk coverage in enterprise deployments.
Five criteria matter most: (1) Threat coverage — alignment with OWASP Top 10 for LLMs and MITRE ATLAS; (2) Extensibility — ability to ingest custom adversarial datasets; (3) Pipeline integration — CI/CD and MLOps compatibility; (4) Remediation actionability — diagnostic depth beyond pass/fail metrics; (5) Multi-modal support — coverage for text, vision, and audio interfaces.
Open-source tools serve different use cases: Garak is a lightweight automated vulnerability scanner using static probes for quick baseline checks. PyRIT (Microsoft) is a Python library for generative AI risk identification, requiring custom engineering to orchestrate. PromptBench focuses on evaluating adversarial prompt robustness and model performance benchmarks. Nora Veridic operates as an enterprise-grade hybrid platform — providing out-of-the-box CI/CD integration, real-time threat intelligence updates, 790+ OWASP ASI-aligned security classes, and automated remediation guidance without the internal engineering overhead of maintaining custom tooling.
OpenAI GPT-4/4o, Anthropic Claude 3.x/4.x, Google Gemini, Meta LLaMA, Mistral, and custom fine-tuned models. We also test multi-model pipelines and RAG architectures.
Traditional pentesting targets known vulnerability classes in deterministic systems. AI red teaming addresses probabilistic, context-sensitive attack surfaces — the same input can succeed or fail depending on conversation history, model version, and system prompt context.
Yes. MCP tool security is a specialist focus — we test for tool injection, malicious tool responses, capability escalation, and cross-server session persistence in agentic MCP pipelines.
AI Red Teaming Framework: Complete Methodology for LLM, RAG, and Agent Security Testing
Singapore-based. Pay after delivery. AI-assisted pipeline with human expert review.