Your AI systems face threats traditional security doesn't cover—prompt injection, data poisoning, model theft, jailbreaks. You've deployed LLMs into production but haven't secured them against attackers who study these systems daily.
Prompt injection attacks can make your LLM execute unintended actions or leak sensitive data.
Your training data could be poisoned—and you'd never know until the model behaves unexpectedly.
Model theft is real. Competitors or attackers can extract your fine-tuned models through careful querying.
Jailbreaks bypass your safety guardrails. New techniques emerge weekly. Your defenses are already outdated.
Comprehensive AI security that addresses threats unique to machine learning systems. Offensive testing meets defensive hardening.
AI-specific threat modeling. Identify attack surfaces across your ML pipeline—training, inference, and deployment.
Red team your AI systems. Prompt injection, jailbreak attempts, data extraction, adversarial inputs.
Implement defenses: input validation, output filtering, rate limiting, anomaly detection, model isolation.
Continuous monitoring for AI-specific threats. Detect prompt injection attempts, unusual query patterns, model drift.
A comprehensive approach to AI security that addresses the unique threat landscape of machine learning systems. Combines offensive testing techniques with defensive hardening strategies.
You've deployed LLMs or ML models in production. You handle sensitive data through AI systems. You want to find vulnerabilities before attackers do. You need AI security expertise, not generic penetration testing.
Traditional pentesting doesn't cover AI-specific attack vectors. Prompt injection, jailbreaks, training data extraction, and adversarial inputs require specialized expertise. I combine traditional security knowledge with deep understanding of ML/LLM internals.
Indirect prompt injection through retrieved data. If your RAG system pulls content from external sources, attackers can embed malicious instructions in that content. Your LLM then executes those instructions, potentially leaking data or taking unauthorized actions.
We use controlled red team testing with agreed scope and rollback procedures. Testing happens in staging environments when possible. For production testing, we use techniques that probe vulnerabilities without causing actual harm—similar to ethical hacking but for AI-specific threats.
No system is fully secure—AI or otherwise. The goal is defense in depth: multiple layers of protection so that if one fails, others catch the threat. We help you achieve appropriate security for your risk profile and use case, not theoretical perfection.
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