Simulate adversarial attacks, prompt injection, and data poisoning against your AI and LLM applications. We identify vulnerabilities that traditional security tools miss, with actionable remediation aligned to OWASP LLM Top 10 (2026) and MITRE ATLAS.
Purpose-built attack scenarios for your models, LLM applications, and MLOps infrastructure.
Evaluate model behaviour against crafted inputs designed to manipulate predictions across images, text, and tabular data.
Identify vulnerabilities in training pipelines that allow malicious samples to degrade or backdoor models.
Simulate API probing attacks to test whether proprietary model weights and architecture can be reconstructed.
Test model resilience against carefully perturbed inputs that bypass classification and detection systems.
Assess third-party model components, pre-trained weights, and training data provenance for tampering and integrity risks.
Evaluate model outputs for discriminatory patterns and fairness violations that create regulatory and reputational risk.
Red-team your LLM apps for direct and indirect prompt injection, jailbreaks, and system prompt extraction.
Test for unvalidated LLM outputs that execute code, access APIs, or trigger backend actions without authorisation.
Assess LLM plugin and tool integrations for unauthorised actions, data access, and privilege escalation paths.
Identify whether your model reveals sensitive training data through extraction, completion, and membership inference attacks.
Test safety filters and guardrails for bypass techniques that produce harmful, biased, or policy-violating content.
Assess RAG pipelines, embedding stores, and LLM orchestration layers for injection and data exposure risks.
Audit CI/CD pipelines for model training and deployment for tampering, unauthorised code execution, and dependency attacks.
Review inference endpoints for authentication gaps, rate limiting, and input validation weaknesses.
Assess model artifact storage and registry for access control gaps, unencrypted weights, and version tampering.
Identify exposed experiment tracking dashboards and metadata leaks that reveal model architecture and hyperparameters.
Audit cloud-based training and inference environments for misconfigurations, over-permissive IAM, and data exposure.
Validate SBOM generation, model cards, and audit trails for ISO 42001 and NIST AI RMF requirements.
A structured six-phase process aligned with OWASP LLM Top 10 (2026) and MITRE ATLAS, from asset inventory through validated remediation.
Map all models, pipelines, datasets, APIs, and third-party AI integrations in scope. Identify high-risk components and data flows that define the testing boundary.
Identify AI-specific threats using STRIDE, MITRE ATLAS, and OWASP LLM Top 10 (2026). Map attack surfaces for model inference, training pipelines, and LLM integrations.
Execute adversarial examples, prompt injection campaigns, jailbreak attempts, and model probing attacks against target systems.
Assess training data integrity, data poisoning vectors, model provenance, and third-party component risks across the ML lifecycle.
Deliver CVSS-scored findings with attack reproductions, mitigation playbooks, and governance recommendations aligned to NIST AI RMF and ISO 42001.
Re-test all critical and high findings after your team applies remediations. Confirm that adversarial attack paths are closed and safety guardrails are effective.
SaaS and startups building AI-powered features, chatbots, recommendation systems, or generative AI integrations that need security validation before release.
Large organisations deploying AI for automation, fraud detection, customer service, or internal decision-making at scale.
Healthcare, FinTech, and government needing AI systems that comply with DPDPA, EU AI Act, HIPAA, or ISO 42001 requirements.