AI Security Reference · OWASP LLM Top 10 · MITRE ATLAS · EU AI Act

AI Security Audit, LLM Red Teaming & Penetration Testing

Engineering methodologies for AI security audits and LLM penetration testing: architecting sovereign AI agents, defending against prompt injection, securing RAG vector databases, testing worst-case adversarial robustness, and certifying compliance under EU AI Act Article 15 and ISO/IEC 42001.

Explore all 19 AI security guides →

Why Architect & Audit Secure AI?

A model achieving 99% benchmark accuracy still breaks under adversarial pressure in production. Without secure-by-design architecture, an attacker can:

  • Hijack LLM and agent behavior via indirect prompt injection
  • Exfiltrate sensitive records from RAG vector databases
  • Steal proprietary weights and IP through API model extraction
  • Plant dormant sleeper triggers via training data poisoning
  • Compromise the AI supply chain (malicious Pickle files, adapters)

The 6 Attack Surfaces of an AI Security Audit

A comprehensive AI penetration test evaluates every layer of the machine learning stack prior to production deployment.

🔴 LLM Red-Teaming & Pentesting

Multi-turn jailbreaks, direct/indirect prompt injection, token smuggling, and bypassing RLHF safety alignments.

LLM Red-Teaming Protocol →

🔴 Model Extraction & Privacy

Black-box API distillation, membership inference attacks (MIA), and training data reconstruction audits.

Model Extraction Guide →

🔴 Data Poisoning & Backdoors

Corrupting fine-tuning corpora, clean-label feature collisions, neural trojans, and dormant sleeper agents.

Data Poisoning Defense →

🔴 AI Supply Chain Security

Unsafe weight deserialization, compromised open-source hubs, poisoned LoRA adapters, and AI-BOM provenance.

Supply Chain Audit →

🔴 AI Guardrails & Runtime Firewalls

Real-time input/output semantic filtering, behavioral drift telemetry, and deterministic circuit-breakers.

Guardrails Architecture →

🔴 Multimodal & Computer Vision

Physical adversarial patches, OCR visual prompt injection, watermark evasion, and sensor spoofing attacks.

Vision Security Audit →

AI Security Standards & Regulatory Audit Matrix

Mapping international AI governance statutes to verifiable adversarial testing controls.

Standard / Regulation Target AI Scope Mandatory Technical Verification & Audit Evidence
EU AI Act (Article 15) High-Risk AI systems (Annex III: Finance, Healthcare, HR) & systemic GPAI models Documented resilience against prompt injection, data poisoning, model inversion, and adversarial evasion.
ISO/IEC 42001:2023 Enterprise AI Management Systems (AIMS) & third-party model governance Algorithmic risk assessments, multi-tenant RAG vector database access controls, and continuous telemetry logs.
OWASP LLM Top 10 Generative AI applications, RAG pipelines, and tool-using agents Penetration testing for Prompt Injection (LLM01), Sensitive Information Disclosure (LLM06), and Excessive Agency (LLM08).
MITRE ATLAS & NIST AI RMF Adversarial threat modeling across ML/DL and autonomous systems Red-teaming campaigns mapped to adversarial tactics, techniques, and procedures (TTPs) and worst-case risk measurement.

Technical AI Security & Architecture Knowledge Base

In-depth engineering guides on secure-by-design architectures, adversarial testing frameworks, and formal verification archives.

Sovereign AI Agent Architecture Secure-by-design local agents, open-weights, MCP tool-calling, and strict memory isolation. /sovereign-ai-agent-architecture/ AI Agent Security Audit Auditing tool-use privileges, MCP integrations, action loops, and confused deputy escalation. /ai-agent-security-audit/ AI System Audit Framework Full-stack 4-layer audit workflow, from model weights and RAG to governance and risk reporting. /ai-audit/ Prompt Injection Attacks Direct and indirect prompt injection, semantic smuggling, and output hijacking mitigations. /prompt-injection-attacks/ RAG & Vector DB Security Securing retrieval-augmented generation against embedding poisoning and cross-tenant data leaks. /rag-vector-db-security/ OWASP LLM Top 10 Guide The canonical taxonomy of large language model application vulnerabilities and defenses. /owasp-llm-top-10/ MITRE ATLAS Framework Adversarial threat matrix, tactics, and procedures for machine learning security testing. /mitre-atlas-framework/ AI Governance, EU AI Act & ISO 42001 High-Risk Annex III conformity, CFPB/ECOA algorithmic fairness, and disparate impact audits. /ai-governance-eu-ai-act-iso-42001/ Adversarial Attacks & RL Gradient-based evasion (FGSM, PGD), policy manipulation, and certified robustness bounds. /adversarial-attacks-reinforcement-learning/ Cyber-Physical & Robotics Security Autonomous vehicles, drones, industrial robotics, and embodied AI threat modeling. /cyber-physical-ai-robotics-security/ Strategic Reasoning & Multi-Agent Game theory, Strategy Logic (SL), Stackelberg equilibria, and multi-agent swarm security. /strategic-reasoning-multi-agent-security/ AI Penetration Testing Protocol Structured red team campaigns, CVSS-AI risk grading, and WASA conference archive. /ai-penetration-testing-audit/

AI Security Audit by Regulated Sector

Adversarial testing protocols tailored to industry-specific threat models and regulatory mandates.

Banking & Finance Credit scoring fairness (EU AI Act Annex III / ECOA), fraud models, and financial chatbots. /banking-ai-security-audit/ Healthcare & Clinical Clinical decision support, HIPAA patient privacy, membership inference, and medical devices. /healthcare-ai-security-audit/ Automotive & Mobility ADAS sensor spoofing, ISO 21434 / UNECE R155 compliance, OTA updates, and in-car assistants. /automotive-ai-security-audit/ Enterprise & Consulting Multi-tenant RAG isolation, client confidentiality, and ISO/IEC 42001 governance. /consulting-ai-security-audit/

Frequently Asked Questions on AI Security Audits

What is an AI security audit (AI penetration test)?

An AI security audit is a structured technical evaluation that tests machine learning models, LLM applications, RAG pipelines, and autonomous agents against adversarial attacks—including prompt injection, data poisoning, model extraction, and unauthorized tool execution.

How does LLM red-teaming differ from traditional pentesting?

Traditional penetration testing targets deterministic software bugs (SQL injection, buffer overflows). LLM red-teaming targets probabilistic decision boundaries, using adversarial natural language, indirect document payloads, and multi-turn games to bypass safety guardrails.

Does the EU AI Act require adversarial security testing?

Yes. Article 15 of the EU AI Act mandates that High-Risk AI systems and systemic General-Purpose AI (GPAI) models achieve verifiable robustness against adversarial attempts to alter their use, outputs, or performance—explicitly covering data poisoning, model flaws, and evasion attacks.

How do you audit an enterprise RAG or multi-agent system?

Auditing an enterprise RAG or agentic workflow requires testing vector store ACL isolation across tenants, injecting indirect payloads into ingested documents, verifying least-privilege constraints on external tool calls, and validating runtime input/output guardrails.