What is AI red teaming and how does it differ from penetration testing?

AI red teaming systematically probes LLMs, RAG pipelines, and AI agents for adversarial failures—prompt injection, data poisoning, model extraction, and agent escalation. Unlike traditional penetration testing, it evaluates emergent model behaviours and AI-specific attack surfaces that have no CVE equivalent.

What is the OWASP LLM Top 10 and why does it matter for AI red teaming?

OWASP LLM Top 10 documents ten critical risk categories for LLM applications: prompt injection, insecure output handling, training data poisoning, model denial-of-service, and supply chain vulnerabilities. AI red teams use it as a structured baseline to ensure full adversarial attack surface coverage.

How do you test RAG pipelines for security vulnerabilities?

RAG red teaming covers retrieval poisoning via injected documents, context-window manipulation, embedding model attacks, and retrieval filter bypass. Each stage—retrieval, augmentation, generation—should be tested independently and as a chain to surface composite vulnerabilities that only emerge at stage boundaries.

How many adversarial test cases are needed for comprehensive LLM security evaluation?

Comprehensive LLM security evaluation needs 500–2,000 adversarial test cases covering OWASP LLM Top 10, MITRE ATLAS TTPs, and application-specific threat models. Innora's AI red teaming framework uses 790+ OWASP ASI security classes covering prompt injection, jailbreaking, and agent kill-chain scenarios.

AI Red Teaming Framework: Complete Methodology for LLM, RAG, and Agent Security Testing

AI red teaming is the structured adversarial testing of LLMs, RAG pipelines, and AI agents to identify exploitable security failures before attackers do. This article documents Innora's 5-stage methodology, our benchmark results across 33+ LLM platforms, and how we map findings to OWASP ASI, MITRE ATLAS, and Singapore regulatory requirements.

What Is AI Red Teaming?

AI red teaming applies adversarial testing principles — adapted from traditional penetration testing — to AI/ML systems. Unlike conventional red teaming that targets code execution and access control, AI red teaming focuses on the unique failure modes of generative models: prompt injection, goal hijacking, context manipulation, data exfiltration through model outputs, and the abuse of tool-use and agent capabilities.

AI red teaming vs. traditional pentesting:

| Dimension | Traditional Pentest | AI Red Teaming | |-----------|--------------------|--------------------| | Target | Code, APIs, network, auth | Model behaviour, prompts, context, tools | | Vulnerability class | CVEs, misconfigs, privilege escalation | Jailbreaks, injection, hallucination exploitation | | Exploit primitive | Shellcode, SQL injection, SSRF | Adversarial prompts, context poisoning, tool abuse | | | High (deterministic code) | Probabilistic (model outputs vary) | | | Code fix, config change | RLHF, guardrails, architectural constraints | | | System boundary | Model boundary + system boundary |

AI Red Teaming Framework: Complete Methodology for LLM, RAG, and Agent Security Testing

What Is AI Red Teaming?

Innora's 5-Stage AI Red Teaming Methodology

Stage 1: Threat Model and Scope Definition

Stage 2: Reconnaissance and Baseline

Stage 3: Adversarial Attack Execution

Stage 4: MCP and Multi-Agent Security Testing

Stage 5: Reporting and OWASP/MITRE Mapping

Innora AI Red Teaming Benchmark: 33+ LLM Platforms

OWASP ASI Top 10 Coverage Map

Singapore Context: AI Red Teaming for Regulated Deployments

Engagement Models

Summary

Feng Ning (风宁)

Related Chronicles

AI Security for Singapore FinTech: MAS TRM, PDPA, and Threat Detection Guide

AI Security for Singapore Critical Infrastructure: Finance, Healthcare, Transport and Cloud

Using eBPF for Real-Time AI Workload Threat Detection in Cloud-Native Environments

Subscribe for AI Security Insights