Adversarial security testing for AI systems
Assess prompt injection exposure, jailbreak resistance, data leakage risks, model and AI integration weaknesses, and adversarial abuse scenarios across enterprise LLM and generative AI environments.
Do you need AI or LLM red teaming?
A quick self-check. If several of these sound like you, it is worth a short conversation.
You likely need this if
- You are shipping features built on LLMs, chatbots or AI agents
- Your AI touches customer data, internal tools, or can take actions for a user
- You are concerned about prompt injection, data leakage or unsafe model behaviour
- You need to evidence AI governance for the EU AI Act, ISO 27001 or customers
Not sure where you land? A short scoping call will tell you plainly, including if you do not need this yet.
Book a scoping callWhat is LLM Red Teaming?
LLM Red Teaming is an adversarial cybersecurity service that systematically identifies how AI systems can be misused. It applies real attacker techniques - jailbreaking, prompt injection, model inversion, training-data poisoning, and adversarial abuse - to your LLMs, AI agents, and the integrations that connect them to data, tools, and users.
Unlike traditional penetration testing that targets static network infrastructure, AI red teaming addresses the probabilistic nature of LLMs, where vulnerabilities live in the interpretive layer of natural language.
Confirm which prompt-injection, jailbreak, and abuse paths actually work against your models and agents.
Test for model inversion, training-data leakage, and PII exposure across your AI workflows.
Probe the tools, plugins, and data sources your LLMs can reach for privilege and data-access abuse.
Generate documented findings that satisfy EU AI Act, DORA, NIS2, and GDPR expectations.
Key technical problems we solve
AI systems fail in ways classic security tools do not test for. We target the abuse paths unique to LLMs and generative AI.
Adversarial prompts that bypass safety guardrails, override system instructions, or coerce the model into unauthorized actions.
Model inversion and extraction techniques that surface sensitive training data, secrets, or other users' information.
Dataset contamination - as little as 1% poisoned data can trigger hidden backdoors - validated through differential analysis.
Abuse of tools, plugins, retrieval sources, and agent actions that connect the model to data and systems.
LLM red teaming in detail
Reconnaissance
Map the AI surface: models, agents, prompts, tools, data sources, and trust boundaries.
Threat Modeling
Identify abuse cases and likely adversary objectives specific to your AI use.
Adversarial Testing
Run jailbreaks, prompt injection, inversion, and poisoning probes against the system.
Exploitation & Impact
Safely demonstrate real impact: data exposure, unauthorized actions, and guardrail bypass.
Reporting & Remediation
Deliver an executive narrative plus developer-ready findings with reproduction steps and fixes.
Every finding comes validated, with reproduction steps, remediation guidance, and the compliance documentation boards and regulators expect.
Secure your AI infrastructure
Sector-specific analysis
Fintech & Banking
The Problem: AI assistants and fraud models touch transactions and customer data, creating new manipulation and leakage paths.
The Outcome: We test prompt injection, data exposure, and integration abuse so AI cannot be turned into a fraud or exfiltration channel.
Software & AI Development
The Problem: Teams shipping LLM features rarely test for adversarial misuse before release.
The Outcome: We red team your models, agents, and plugins pre-release, giving developers precise, reproducible fixes.
Critical Infrastructure
The Problem: AI in operational or decision-support roles can be coerced into unsafe actions or outputs.
The Outcome: We validate guardrails, access boundaries, and integration security so AI cannot become a pivot into critical systems.
Use cases
Prompt Injection Defense
Validate that untrusted input cannot override system instructions or trigger unauthorized tool use.
Jailbreak Resistance
Measure how well safety guardrails hold up against current jailbreak and obfuscation techniques.
PII Leakage Mitigation
Probe for training-data and context leakage that exposes personal or confidential information.
Exfiltration Testing
Test whether an attacker can chain model access and integrations to extract data or secrets.
Reporting structure and metrics
Management Report
An executive view of AI risk, business impact, compliance alignment, and a prioritized remediation roadmap for board review.
Technical Report
Developer-ready findings with adversarial prompts, reproduction steps, affected components, severity, and fixes.
Reduction in adversarial prompt success rate, previously unknown backdoor triggers found, and improved time-to-detect for novel manipulation techniques.
Protect against AI threats now
Find the prompt-injection, jailbreak, and data-leakage paths in your AI before attackers do. Get a tailored LLM red teaming proposal in less than 48 hours.
Regulatory & compliance deep dive (EU focus)
Our AI red teaming produces the documented, independent evidence that EU regulations increasingly expect for AI systems.
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EU AI Act (Art. 46): Documented adversarial testing for general-purpose models with systemic risk.
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DORA (Art. 24-25): Threat-led penetration testing (TLPT) for financial ICT resilience.
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NIS2 (Art. 21): Supply-chain security validated with real attacker tactics, techniques, and procedures.
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GDPR (Art. 35): Red teaming as part of Data Protection Impact Assessments (DPIAs) for AI processing.
Red teaming FAQ
Key mandates include:
- EU AI Act (Art. 46): documented testing for general-purpose models with systemic risk.
- DORA (Art. 24-25): threat-led penetration testing (TLPT) for financial ICT resilience.
- NIS2: supply-chain security implemented via real attacker TTPs for essential service operators.
- GDPR (Art. 35): red teaming as part of Data Protection Impact Assessments (DPIAs) for AI processing.
Sufficient evidence includes:
- Audit trails: detection timestamps and notification dispatch records (e.g., DORA reporting windows).
- Technical proof: vulnerability remediation tracking and verification testing of security fixes.
- Governance: board-level attestation of security posture and updated policies reflecting lessons learned from simulations.