Security & AI
As generative AI systems are rapidly set up by organizations, new attack surfaces open that their traditional security controls cannot handle. From prompt injection attacks to exfiltration through embeddings, AI-specific threats demand specialized offensive security expertise.
Gen AI Red Teaming
Adversarial Testing for AI System Resilience
Our Gen AI Red Teaming service simulates real-world attacks against your AI systems to proactively find the vulnerabilities before adversaries exploit them. Unlike traditional penetration testing, AI red teaming addresses non-deterministic system behavior, model-specific threats, and multi-stage attack chains.
What We Test
- Prompt Injection Attacks – Direct and indirect prompt manipulation to bypass system instructions
- Jailbreak Attempts – Techniques to circumvent safety controls and content filters
- Data Exfiltration – Extraction of training data, sensitive information, and PII through clever prompting
- Context Poisoning – Manipulation of conversation history and RAG data sources
- Model Manipulation – Adversarial inputs designed to alter model behaviour or outputs
- Agent-to-Agent Exploitation – Multi-stage attacks across autonomous AI agents
- Bias and Fairness Testing – Identifying discriminatory outputs and ethical vulnerabilities
Our Approach
- Threat Modeling – Mapping attack surfaces specific to your AI implementation
- Adversarial Simulation – Executing thousands of attack scenarios using automated and manual techniques
- Behavioural Risk Analysis – Testing for hallucinations, toxic outputs, and unintended behaviours
- Lifecycle Coverage – Continuous assessment across development, staging, and production environments
Deliverables
- Comprehensive vulnerability report with CVSS scoring for AI-specific risks
- Attack scenario documentation with reproduction steps
- Prioritized remediation roadmap aligned with OWASP Top 10 for LLM
- Executive summary for CISO and board-level communication
Gen AI Penetration Testing
In-Depth Security Assessments for LLM Applications
Our Gen AI Penetration Testing service provides thorough security evaluations of LLM-powered applications, APIs, and integrated systems. We combine AI-specific attack methodologies with traditional web/API security testing to uncover vulnerabilities across your entire AI application stack.
Testing Scope
- AI-Specific Vulnerabilities (OWASP Top 10 for LLM):
- LLM01: Prompt Injection
- LLM02: Insecure Output Handling
- LLM03: Training Data Poisoning
- LLM04: Model Denial of Service
- LLM05: Supply Chain Vulnerabilities
- LLM06: Sensitive Information Disclosure
- LLM07: Insecure Plugin Design
- LLM08: Excessive Agency
- LLM09: Overreliance
- LLM10: Model Theft
Integration & Infrastructure Testing
- API security for LLM endpoints
- Authentication and authorization flaws
- Vector database security assessments
- RAG (Retrieval-Augmented Generation) pipeline vulnerabilities
- Model serving infrastructure weaknesses
- Cloud misconfigurations (AWS, Azure, GCP)
- Third-party AI service integrations
Methodology
- Reconnaissance – Asset discovery, architecture mapping, and threat surface analysis
- Dynamic Testing – Manual and automated security assessments using specialized AI/LLM tools
- Exploitation – Validation of findings with proof-of-concept attacks
- Post-Exploitation Analysis – Assessing impact of successful attacks on data confidentiality, integrity, and availability
- Remediation Guidance – Detailed fix recommendations with secure code examples
Testing Frameworks
- OWASP LLM Application Security Verification Standard (ASVS)
- NIST AI Risk Management Framework
- MITRE ATLAS (Adversarial Threat Landscape for AI Systems)
- Custom threat modeling based on your use case
Deliverables
- Detailed technical report with vulnerability descriptions, evidence, and remediation steps
- Risk assessment with business impact analysis
- Compliance mapping (GDPR, HIPAA, PCI DSS considerations for AI)
- Retest validation after remediation
Gen AI Guardrail Validation
Ensuring Your AI Safety Controls Actually Work AI guardrails are the policy, technical controls, and monitoring mechanisms that keep your AI systems operating safely within defined boundaries. But 87% of enterprises lack comprehensive AI security frameworks, and many deployed guardrails fail under adversarial pressure. Our Guardrail Validation service rigorously tests your AI safety controls to ensure that they effectively prevent harm, keep sensitive data safe, and maintain compliance without creating operational bottlenecks.
What We Validate
- Input Filtering & Sanitization:
- Malicious prompt detection and blocking
- Input length and complexity controls
- Multi-language and encoding bypass attempts
- Output Safety Controls:
- Content filtering effectiveness (toxic, harmful, offensive content)
- Hallucination detection and mitigation
- PII leakage prevention in responses
- Copyright and IP protection mechanisms
- Access Controls & Authorization:
- Role-based access control (RBAC) for AI features
- Session management and context isolation
- API rate limiting and abuse prevention
- Multi-tenancy security boundaries
Validation Process
- Guardrail Documentation Review – Understanding your implemented controls and policies
- Bypass Testing – Attempting to circumvent safety controls using adversarial techniques
- Load Testing – Validating performance under high-volume attack scenarios
- False Positive Analysis – Assessing impact on legitimate use cases
Deliverables
- Guardrail effectiveness scorecard with pass/fail criteria [Policy violation and forward deflection]
- Bypass technique documentation with evidence
- Gap analysis against industry best practices
- Optimization recommendations to balance security and usability
