Find the vulnerabilities in your AI before attackers do
Your AI system will be attacked. Is it ready?
Find where your AI fails before your adversaries do.
Adversarial testing for LLMs, GenAI systems, and AI agents. T3 applies structured attack taxonomies, MITRE ATLAS tactics, and real-world adversary simulation to surface failure modes, safety gaps, and exploitable behaviours before they reach production.
Which organisations need adversarial AI testing?
If your organisation builds, deploys, or procures AI systems that interact with users, process sensitive data, or inform decisions, adversarial testing is not optional. Here is who we work with and why.
High-risk AI operators
Organisations deploying AI in regulated or safety-critical contexts: financial services, healthcare, critical infrastructure, legal, and government. Required under the EU AI Act for high-risk AI systems.
- Credit scoring, underwriting, or fraud detection models
- Clinical decision support or triage AI
- AI-assisted recruitment or performance management
- AI systems subject to EU AI Act, DORA, or FCA PS22/3
GenAI and LLM product teams
Teams building products on top of foundation models, including customer-facing chatbots, AI agents, RAG pipelines, and copilot features embedded in enterprise software.
- Customer-facing LLM products or AI agents
- RAG systems accessing internal or sensitive data
- Multi-agent pipelines with tool-use or external API calls
- AI copilots embedded in SaaS or internal tooling
Procurement and third-party assurance teams
Risk, compliance, and procurement functions that need independent assurance before onboarding an AI vendor or certifying a supplier's AI system for use.
- Vendor AI due diligence and supply chain risk
- Pre-procurement AI system evaluation
- Post-deployment assurance reviews on contracted AI systems
- ISO/IEC 42001 or EU AI Act Article 9 conformity evidence
What is adversarial testing for AI?
Adversarial testing means deliberately attempting to make an AI system behave in ways it should not. Unlike functional QA, which tests whether the system does what it is supposed to do, adversarial testing probes what the system does when subjected to inputs designed to confuse, manipulate, or subvert it.
For traditional software, adversarial testing overlaps with penetration testing and fuzz testing. For AI systems, the attack surface is fundamentally different: the model itself is a statistical function, and its failure modes emerge from its training data, its architecture, and the way it has been fine-tuned or prompted.
Modern AI adversarial testing draws on three bodies of work: classical ML security research (evasion, poisoning, inference attacks), red teaming practices from cybersecurity, and emerging frameworks specifically designed for large language models and agentic AI systems.
Why this matters now: the incident data
Adversarial attacks on AI systems are no longer a research curiosity. Reported incidents have climbed sharply, attack techniques have grown more sophisticated, and the success rate of the most common attacks remains high even against hardened models.
Sources: Stanford HAI AI Index 2025; AI Incident Database (incidentdatabase.ai) via Our World in Data. 2025 figure (*) is a partial-year estimate extrapolated from the reported 56.4% growth rate. Annual totals vary by database and reporting methodology; chart illustrates the reported trend direction.
Approximate values from Figure 2, International AI Safety Report 2025 (arXiv 2511.19863). Model identities not disclosed in source; values illustrate the downward trend described. Verify before citing in regulatory submissions.
Source: The Promptware Kill Chain (arXiv 2601.09625, 2026). Covers 21 classified incidents in the 2025 to 2026 maturation period; not a complete census.
Adversarial attack categories
Six families of adversarial attack, mapped to MITRE ATLAS and the OWASP LLM Top 10, covering the full threat surface of modern AI systems.
Prompt Injection
Malicious instructions embedded in user input or retrieved context that override or subvert the model's intended behaviour. The most prevalent class of LLM vulnerability in production deployments.
Attacker-controlled user input that directly instructs the model to override its system prompt or produce disallowed output.
MITRE AML.T0051Malicious instructions hidden inside documents, web pages, or database entries retrieved by a RAG pipeline, executing when the LLM processes the retrieved content.
OWASP LLM01Attacks that build across several conversation turns, incrementally priming the model to produce a disallowed response that would be refused in a single-turn exchange.
MITRE AML.T0054Techniques that cause the model to reveal the contents of its confidential system prompt, disclosing proprietary instructions, safety guardrails, or internal configuration.
OWASP LLM07Jailbreaking & Guardrail Bypass
Techniques that cause a model to ignore its safety fine-tuning and produce outputs it was explicitly trained or instructed not to produce.
Instructing the model to act as a fictional persona, alternative AI system, or character that does not share its safety constraints.
OWASP LLM01Obfuscating disallowed content through deliberate misspellings, character substitutions, base64 encoding, or other token-level transformations that bypass string-matching filters.
MITRE AML.T0051.001Providing a large number of example exchanges that normalise disallowed behaviour, exploiting the model's tendency to continue patterns it observes in context.
OWASP LLM01Computationally derived adversarial suffixes (Greedy Coordinate Gradient and variants) appended to prompts that reliably elicit unsafe outputs even from safety-tuned models.
MITRE AML.T0051.002Data & Model Attacks
Attacks targeting the training pipeline, model weights, or inference-time data, rather than the prompt interface.
Corrupting the training dataset with adversarially crafted examples to introduce backdoors, biases, or degraded performance on specific inputs.
MITRE AML.T0020Determining whether a specific data record was included in the model's training set, a significant privacy risk for models trained on personal or confidential data.
MITRE AML.T0024Using model outputs to reconstruct or approximate training data, recovering sensitive records or proprietary information from a deployed model's responses.
MITRE AML.T0024.001Querying a model repeatedly to reproduce its functionality in an independently trained surrogate, enabling IP theft or offline adversarial attack development.
MITRE AML.T0049Agentic & Multi-Agent Attacks
Attacks that exploit the tool-use, planning, and inter-agent communication capabilities of AI agents and multi-agent pipelines.
Crafting inputs that cause an AI agent to invoke its connected tools (APIs, databases, code execution) in unintended or harmful ways, escalating the impact beyond the LLM's output.
OWASP LLM06Environmental injection attacks that redirect an autonomous agent's goal mid-task, causing it to pursue an attacker-defined objective while appearing to continue normal operation.
MITRE AML.T0051.003Exploiting inter-agent trust relationships so a lower-privilege agent instructs a higher-privilege agent to take actions it would otherwise refuse.
OWASP LLM09Prompt injection attacks that instruct an agent to leak sensitive data through an outbound channel such as a URL, external API call, or user-visible output field.
OWASP LLM02Supply Chain & Third-Party Attacks
Attacks that enter through the AI system's dependencies: foundation models, plugins, vector databases, and fine-tuning datasets sourced from third parties.
Pre-trained or fine-tuned models distributed with hidden triggers that activate specific behaviours when a particular input pattern is encountered in production.
MITRE AML.T0019Adversarially crafted embeddings inserted into a RAG knowledge base such that specific retrieval queries consistently return attacker-controlled content.
OWASP LLM03Third-party plugins or tool integrations that exfiltrate context, perform unintended actions, or inject adversarial content into the model's context window.
OWASP LLM05Poisoning the fine-tuning dataset used to adapt a foundation model, inserting backdoors or biases that persist in the deployed fine-tuned variant.
MITRE AML.T0020.003Evasion & Adversarial Inputs
Inputs engineered to fool AI classifiers, detectors, or decision systems without the manipulation being visible to human reviewers.
Imperceptibly modified images that cause a vision model to misclassify, misidentify objects, or produce incorrect outputs, with direct applications to fraud detection and identity verification.
MITRE AML.T0029Structuring text or multimodal inputs to pass safety classifiers and content moderation filters while still delivering harmful or policy-violating content.
MITRE AML.T0015Using linguistic transformations such as synonym substitution, paraphrase, or non-Latin scripts to bypass keyword and embedding-based safety classifiers.
OWASP LLM01Inputs engineered to maximise model compute consumption, causing latency degradation or cost-based denial of service in pay-per-token deployments.
OWASP LLM10How the attack surface is evolving
Two patterns define the current landscape: prompt injection dominates documented attacks, and the structured knowledge of how to attack AI is expanding rapidly. The MITRE ATLAS framework has grown to keep pace.
Research estimates from classified incident samples, not official statistics. Sources: arXiv 2511.21901; arXiv 2601.09625. Percentages reflect prevalence, not mutually exclusive shares.
Bars indicate relative direction and momentum, not absolute share. The MIT AI Incident Tracker classifies AIID incidents against the MIT Causal and Domain taxonomies and EU AI Act risk levels; it presents results as an interactive timeline rather than fixed percentages, so categories above reflect the trends its editors report rather than a static breakdown.
Sources: AI Incident Database (incidentdatabase.ai); MIT AI Incident Tracker (airisk.mit.edu); reporting in TIME, April 2026. AIID is crowd-sourced and likely undercounts incidents; figures are continuously revised. Verify current totals at incidentdatabase.ai before citing in regulatory submissions.
~30 techniques
~54 techniques
+14 agent TTPs
56 sub-techniques
In October 2025, MITRE and Zenity Labs added 14 techniques specifically for AI agents and generative AI, covering context poisoning, memory manipulation, and agent hijacking. The framework now catalogues 16 tactics and 84 techniques.
Sources: MITRE ATLAS v5.1.0 (November 2025); Vectra AI and Practical DevSecOps coverage analysis, 2026. Verify the current version at atlas.mitre.org before citing technique IDs in regulatory submissions.
- Single-stage attacks only
- Direct prompt injection and jailbreak
- No persistence or lateral movement
- Targets: web AI, search, chatbots
- Multi-stage kill chains emerge
- 5 of 12 showed persistence
- 3 of 12 showed lateral movement
- Morris II: first 5-stage attack (Mar 2024)
- 4+ stage attacks now routine
- Coding assistants: 7 of 21 incidents
- Promptware-native C2 emerging
- CVE-2025-53773: Copilot RCE (CVSS 9.6)
Source: The Promptware Kill Chain (arXiv 2601.09625, January 2026), a peer-reviewed analysis of documented real-world incidents.
Prompt injection is unavoidableIt is the most prevalent vector, with a ~50% success rate even against hardened 2025 models. If your system processes any external content, it is exposed.
Coding assistants are the new front lineThe fastest-growing target category. Any organisation using LLM-assisted development faces material supply chain and credential theft risk.
Yesterday's test is out of dateAttack sophistication is accelerating faster than defences. Techniques that needed research skill in 2023 are now commoditised. Point-in-time testing ages quickly.
Classical adversarial testing approaches
These established methodologies form the structured backbone of any rigorous adversarial evaluation programme. Each addresses a different dimension of AI system risk.
Red teaming
A structured exercise in which a dedicated adversarial team attempts to find failures, safety violations, and exploitable behaviours in an AI system. Red teaming is human-led, goal-directed, and qualitative in character. It is the primary method recommended by the NIST AI RMF (AI RMF 1.0, GOVERN 1.1) and referenced in EU AI Act recitals for high-risk and general-purpose AI.
NIST AI RMF 1.0- Adversarial scenario library
- Graded failure register
- Exploitability assessment
Automated adversarial evaluation
Systematic, programmatic testing at scale using adversarial datasets, automated attack pipelines (such as PyRIT, Garak, or custom harnesses), and evaluation benchmarks. Complements human red teaming by achieving coverage across large attack spaces that manual testing cannot reach.
OWASP LLM Top 10- Attack success rate metrics
- Reproducible test harnesses
- CI/CD integration artefacts
Prompt evaluation and benchmarking
Systematic assessment of model behaviour across controlled prompt sets, including safety benchmarks (such as HarmBench, TrustLLM, and MT-Bench adversarial variants) and domain-specific evaluation suites. Produces quantitative scores that support before-and-after comparison and regulatory evidence.
EU AI Act Article 9- Benchmark scorecards
- Regression baselines
- Audit-ready evidence pack
Bias and fairness testing
Structured evaluation of model outputs across protected characteristics and demographic groups, identifying discriminatory patterns, disparate impact, and distributional fairness failures. Required under EU AI Act Annex III for high-risk AI and relevant to UK Equality Act obligations for AI-assisted decisions.
ISO/IEC 42001- Disparate impact analysis
- Counterfactual fairness probes
- Mitigation recommendations
Privacy and data security testing
Testing for training data memorisation, membership inference vulnerability, PII leakage through model outputs, and RAG-pipeline data exposure. Directly relevant to UK GDPR Article 25 (data protection by design) and AI Act Article 10 requirements for data governance.
UK GDPR Article 25- Memorisation audit
- PII exposure report
- DPIA-supporting evidence
Explainability and robustness testing
Evaluating the consistency, stability, and interpretability of model decisions under input perturbation. Addresses robustness to distribution shift and adversarial perturbation, and produces explanations suitable for regulatory and audit audiences.
NIST AI RMF MANAGE- Perturbation sensitivity maps
- Decision explanation report
- Drift detection baselines
MITRE ATLAS: the adversarial AI threat knowledge base
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is the principal structured taxonomy of adversarial attack techniques against AI and ML systems. Developed by MITRE in collaboration with AI security researchers and practitioners, it mirrors the structure of ATT&CK for enterprise systems.
T3 maps every finding from an adversarial engagement to the relevant MITRE ATLAS tactic and technique. This gives you a common language for communicating risk to security teams, technical leads, and boards, and provides evidence suitable for regulatory and audit submissions.
Please verify: MITRE ATLAS is publicly available at atlas.mitre.org. Check for version updates before citing specific technique IDs in regulatory submissions.
ATLAS Tactic Coverage (illustrative)
Where is your organisation on the adversarial testing maturity curve?
Before planning a testing programme, it helps to understand your current baseline. This five-level maturity model maps common organisational states, from no testing at all through to continuous adversarial assurance embedded in the AI development lifecycle.
When do you need to test? Triggers for initiating an adversarial engagement
Before deployment
- New AI system or model entering production
- EU AI Act conformity assessment required
- New high-risk use case or deployment context
During the AI lifecycle
- New model version, fine-tune, or RLHF update
- New tools, plugins, or external integrations added
- Expansion to new user population or market
Reactive triggers
- Known attack on a comparable system published
- Incident, unexpected model behaviour, or user complaint
- Regulatory enquiry or audit requirement received
Adversarial testing is not a one-time exercise
The threat landscape for AI systems evolves continuously. New attack techniques are published, models are updated, and deployment contexts change. Testing programmes need to reflect this.
After every significant model change
Fine-tuning, RLHF updates, adapter swaps, or prompt engineering changes can alter the model's safety boundaries in ways that are not apparent from functional testing alone. Rerun the relevant attack categories after any update that touches model behaviour.
On a defined periodic cadence
Even without model changes, the adversarial threat landscape changes. New jailbreak techniques, new MITRE ATLAS techniques, and new tooling published by the research community may expose previously unknown gaps. A periodic cadence of six to twelve months for established systems is a reasonable baseline; high-risk systems warrant more frequent review.
After a known incident or published vulnerability
When a new class of attack is demonstrated publicly against a comparable model or system, targeted testing against that technique should be prioritised. The time between a technique being published and it being weaponised in real deployments is increasingly short.
When deployment context changes
A system approved for internal use may face a substantially different threat profile when opened to external users, integrated with new data sources, or deployed in a new geography or regulatory jurisdiction. Scope changes require reassessment.
How T3 structures an adversarial engagement
Every T3 adversarial engagement is scoped, structured, and delivered against a documented methodology. Findings are graded by exploitability and impact, mapped to MITRE ATLAS, and delivered in a format suitable for technical, risk, and regulatory audiences.
Define scope, assets, and adversary model
We map the system under test: model type, deployment architecture, tools and integrations, data flows, and trust boundaries. We identify the relevant threat actors and their capabilities, map applicable regulatory requirements, and agree on rules of engagement. This phase determines which attack categories are in scope and which framework the engagement will be assessed against.
- Scoping document
- System threat model
- Adversary profile
- Test plan and rules of engagement
Map the attack surface
We probe the system to understand its behaviour, identify trust boundaries, and enumerate the available attack surface. This includes API enumeration, prompt structure analysis, tool inventory (for agentic systems), and retrieval system inspection (for RAG deployments). Black-box, grey-box, and white-box modalities are applied as agreed in the test plan.
- Attack surface map
- Trust boundary diagram
- Reconnaissance findings log
Execute structured attacks across agreed categories
The core testing phase. Our team executes adversarial scenarios from the agreed attack categories, combining manual red teaming with automated evaluation pipelines. Each successful attack is documented with a reproducible proof of concept, severity grading, and the conditions required for exploitation. All findings are mapped to MITRE ATLAS tactics and techniques and to the OWASP LLM Top 10 where applicable.
- Adversarial scenario log
- Attack success register
- MITRE ATLAS mapping
- Reproducible PoC artefacts
Quantify coverage and calibrate severity
We run the system against applicable adversarial benchmarks to produce quantitative scores alongside the qualitative red team findings. Benchmarks are selected based on model type and deployment context. Results are compared against industry baselines where available and used to produce a maturity-level assessment and a risk-graded finding register.
- Benchmark scorecards
- Maturity level assessment
- Risk-graded finding register
Deliver findings and a structured remediation roadmap
We produce three deliverables: a technical report for engineering teams (with reproducible PoCs and remediation guidance), an executive summary for boards and risk committees (with business impact framing), and a regulatory evidence pack for audit or conformity assessment submissions. We then support remediation through a structured fix review and a retest of critical findings.
- Technical findings report
- Executive summary
- Regulatory evidence pack
- Remediation roadmap
- Fix review and retest
What makes T3 different
We are practitioners who helped write the standards we test against. Our team spans AI security research, regulatory policy, and engineering.
Policy meets engineering
Our team contributed to the development of EU AI Act guidance and ISO/IEC 42001. We translate regulatory obligations into testable technical controls, and testing findings into language that regulators and boards understand.
Current threat coverage
We maintain an active research practice tracking the latest adversarial techniques, published jailbreaks, and MITRE ATLAS updates. Our test cases reflect the current threat landscape, not last year's.
Audit-ready deliverables
Findings are structured to support EU AI Act Article 9 risk management documentation, ISO/IEC 42001 conformity evidence, and internal audit submissions. We know what regulators and auditors look for, because we have worked with them.
Trusted by BigTech and regulated industries
Two thirds of BigTech companies have worked with T3 on AI assurance. We also support financial services firms, healthcare organisations, and public sector bodies where the stakes of AI failure are highest.
Engineering-level remediation
We do not just report findings. We work with your engineering teams to implement defensible mitigations, including prompt hardening, output filtering architecture, agent permission model design, and monitoring instrumentation for post-deployment detection.
Continuous programme support
Beyond point-in-time engagements, T3 can embed adversarial testing into your AI development lifecycle as a continuous programme: test harnesses in CI/CD, a maintained adversarial scenario library, and a standing retesting cadence aligned to your release schedule.
Ready to find where your AI fails before it matters?
A 45-minute structured call. No obligation. No sales pitch. We will review your AI system context, identify the most relevant testing approach, and tell you what good looks like for your use case.
What we cover in the first call
- Your AI system architecture, deployment context, and threat profile
- Which attack categories are most relevant and highest priority
- Your current maturity level and what a testing programme looks like for you
- Applicable regulatory requirements and how testing evidence supports them
- Indicative engagement scope, timeline, and what to expect from T3
Red Team Program Readiness & Maturity Assessment
Assess the maturity of your red teaming program across four key domains
Red Teaming
What We Simulate
What You Get
Mitigate Vulnerabilities with LLM Security Testing
This service goes beyond simple red teaming. We provide technical, strategic, and compliance-grade outputs, including LLM Security Testing, built to serve security leads, AI owners, and audit/compliance teams alike. Whether you’re seeking a snapshot or a full campaign, every engagement comes with clear evidence, regulator-ready reporting, and actionable next steps.
Red Team Simulation Report
Misuse and vulnerability matrix
Clear remediation actions
Optional
Our Impact on AI Adoption
Why T3 for AI Readiness Assessment?
T3 is an award-winning Responsible AI advisory and implementation partner that translates cutting-edge research into practical, safe, deployable AI systems.
- Shaped major global standards and policy (EU AI Act, ISO/IEC 42001, NIST AI RMF, OECD AI Principles, G7 AI Code of Conduct)
- Advised 2/3 of the world’s leading Big Tech organisations
- Trained 50+ board members and advised 20+ governments
- Led by senior AI operators: the founder of Google’s Responsible Innovation & Ethical ML teams (Responsible AI at scale) and Oracle’s former Chief Data Scientist (global AI/ML build-out)
- Winner of 3 AI awards in 2025 (including AI Leader of the Year, Top 33 Women Shaping the Future of Responsible AI, and North America AI Leader of the Year)
We bridge business ambition with engineering excellence.
This red teaming service is tailored for
Who This Is For?
Tech & Risk/Compliance Teams
Regulated sectors
AI and product teams
Public & Private Companies
Frequently Asked Questions
AI red teaming is a structured, adversarial testing approach used to uncover vulnerabilities in AI systems such as LLMs. It simulates attacks like prompt injection, jailbreaks, and misuse to identify weaknesses before they’re exploited in the wild.
Penetration testing targets infrastructure and network layers. Red teaming for AI focuses on model behavior — such as how inputs can be manipulated to cause unintended or unsafe outputs.
We recommend red teaming before every major model release or third-party deployment, and at least quarterly for high-risk systems — aligning with regulatory expectations under DORA, GDPR, and the EU AI Act.
Yes. We support testing for internal LLMs, fine-tuned proprietary models, and third-party tools like OpenAI, Claude, Gemini, and open-source deployments like LLaMA and Mistral.
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Royal Hansen, VP of Privacy, Safety & Security Engineering, Google
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