ADVERSARIAL AI RED TEAMING
AI & LLM Security Assessment
Offensive security assessment of AI systems, LLMs, and agentic workflows — validating resilience against prompt injection, data poisoning, model theft, and supply-chain compromise.
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AI systems introduce attack surfaces that traditional security testing cannot reach. We combine adversarial ML red teaming with offensive security tradecraft to validate LLM resilience, agentic AI trust boundaries, model integrity, and infrastructure hardening — mapped to OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF.
01
PROMPT ATTACK SURFACE
Prompt Injection & Jailbreak Assessment
Tests whether LLM-powered systems can be manipulated to bypass safety controls, leak system instructions, or execute unintended actions through adversarial prompts.
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Direct prompt injection — jailbreak techniques that override system instructions and safety guardrails
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Indirect prompt injection — poisoned documents, web content, and tool descriptions that hijack model behaviour
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Multi-stage and chained attacks — sequential prompt sequences that bypass single-turn defences
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Multimodal injection — adversarial content hidden in images, audio, or mixed-modality inputs
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System prompt extraction — techniques to leak proprietary instructions and internal configuration
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PROMPT ATTACK VECTORS
Direct Jailbreak & Instruction Override
Direct
Indirect Injection via External Content
Indirect
Multi-Turn & Chained Exploitation
Chained
Multimodal & Cross-Modality Attacks
Multi-Modal
OWASP LLM01
JAILBREAK
IPI
MULTI-MODAL
02
DATA INTEGRITY & MODEL PROTECTION
Model & Data Security
Assesses the integrity of training data, model weights, and supply-chain dependencies to prevent poisoning, theft, and undetected drift.
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Data poisoning — backdoor insertion into training pipelines, label manipulation, and trigger-based misclassification
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Training data leakage — membership inference and data extraction attacks exposing sensitive records from model outputs
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Model theft — query-based model extraction to approximate proprietary weights, architecture, and decision boundaries
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Supply-chain integrity — pre-trained model registries, third-party libraries, and dependency vulnerabilities
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Model drift detection — behavioural monitoring to identify silent accuracy degradation or adversarial exploitation
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MODEL & DATA THREAT SURFACE
Training Data Poisoning & Backdoors
Integrity
Membership Inference & Data Extraction
Privacy
Model Theft & Weight Extraction
IP Risk
Supply-Chain & Dependency Compromise
Supply
POISONING
LEAKAGE
MODEL THEFT
SUPPLY CHAIN
03
AGENTIC WORKFLOWS & SERVING INFRASTRUCTURE
Agentic AI & Infrastructure Security
Validates trust boundaries, tool-use controls, and infrastructure hardening across AI agents, RAG pipelines, and model-serving environments.
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Agentic workflow abuse — tool poisoning, privilege escalation across agent chains, and confused-deputy attacks
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RAG pipeline security — context injection through poisoned knowledge bases, retrieval manipulation, and grounding bypass
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MCP & tool-call validation — trust boundary enforcement, credential exposure, and unauthorised action execution
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Inference API hardening — authentication, rate-limiting, error leakage, and resource-exhaustion testing
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Shadow AI discovery — identification of unapproved AI tools processing sensitive enterprise data
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AGENTIC & INFRASTRUCTURE VECTORS
Agent Chain & Tool Poisoning
Agentic
RAG Pipeline & Knowledge Injection
RAG
MCP Gateway & Trust Boundaries
MCP
Inference API & Serving Infrastructure
Infra
Shadow AI & Data Exposure
Ops
AGENTIC AI
RAG
MCP
SHADOW AI
04
BIAS, FAIRNESS & REGULATORY READINESS
AI Governance & Compliance
Evaluates bias, fairness, and regulatory alignment to ensure AI deployments meet emerging governance standards and reduce reputational exposure.
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Bias & fairness testing — demographic benchmarking, disparate-impact analysis, and fairness-violation documentation
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OWASP LLM Top 10 — comprehensive mapping across all ten risk categories for production LLMs
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MITRE ATLAS alignment — adversarial technique coverage mapped to the ATLAS knowledge base
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Regulatory readiness — NIST AI RMF, ISO/IEC AI Security, and EU AI Act compliance assessment
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Responsible AI assurance — output moderation, content policy validation, and hallucination-risk evaluation
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FRAMEWORK & STANDARDS MAPPING
OWASP LLM Top 10 (2025)
Risk
MITRE ATLAS
Adversarial
NIST AI RMF & ISO/IEC AI Security
Standards
EU AI Act Readiness
Regulatory
OWASP LLM
MITRE ATLAS
NIST AI RMF
EU AI ACT
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OWASP LLM Top 10 mapped MITRE ATLAS aligned Adversarial red teaming
Sample Attack Chain Scenario

How a Full AI Attack Unfolds in Five Steps

No infrastructure breach required. This is how attackers chain AI weaknesses into a complete compromise — from poisoned data to stolen models and regulatory fines.

01

Poison the Pipeline

Attackers introduce poisoned samples into an open data source you rely on. These contain hidden triggers that remain dormant until the model is deployed.

02

Drift in the Dark

As retraining occurs, poisoned data slowly alters model logic. Because drift detection isn't tuned for adversarial shifts, the manipulation goes unnoticed.

03

Prompt Injection Bypass

The attacker engages your chatbot using a multi-turn injection chain to override safety filters and extract internal system prompts.

04

Model Extraction & Inversion

With repeated queries, the attacker reconstructs embeddings and regenerates fragments of sensitive training data, compromising both IP and personal information.

05

Exploit & Monetize

The stolen model is cloned and resold. Sensitive data surfaces on underground forums. Your enterprise faces regulatory fines, reputational loss, and IP theft — all from overlooked AI weaknesses.