AI-powered applications introduce a new class of vulnerabilities that traditional security tools cannot detect. We assess, harden, and monitor your AI systems against prompt injection, model abuse, data leakage, and adversarial attacks.
The OWASP LLM Top 10 and beyond β every attack vector unique to AI applications, systematically addressed.
Attackers embed malicious instructions in user input or retrieved content to override your system prompt and make your AI behave in unauthorized ways.
AI-generated content passed to downstream systems β databases, browsers, APIs β without sanitization, enabling XSS, SQLi, and command injection through the LLM layer.
Manipulation of fine-tuning datasets to introduce backdoors, biases, or malicious behaviors into your custom AI models before they ever reach production.
Extraction of sensitive training data, system prompts, or proprietary business logic through crafted queries that cause the model to reveal what it should not.
Abuse of AI agents granted broad tool access β tricking them into deleting data, sending unauthorized communications, or triggering unintended API actions.
Carefully crafted inputs designed to cause misclassification, bypass content filters, or degrade model performance β undermining the reliability of your AI features.
Vulnerabilities in third-party models, plugins, APIs, and datasets your AI application depends on β assessed across your full AI dependency chain.
High-cost prompt attacks, resource exhaustion, and systematic abuse of your AI endpoints β with rate limiting, anomaly detection, and throttling controls to defend against them.
We work across the full AI security lifecycle β finding vulnerabilities, implementing controls, and maintaining ongoing defense.
A structured review of your AI application's attack surface β covering architecture, prompt design, data flows, tool integrations, and output handling β mapped to the OWASP LLM Top 10.
Our specialists attempt to break your AI system using real attacker techniques β prompt injection chains, jailbreak attempts, data extraction probes, and agent manipulation scenarios.
Design and deployment of input/output filters, content classifiers, semantic guardrails, and behavioral monitors that block malicious use without degrading legitimate functionality.
Continuous logging and analysis of AI interactions to detect novel attack patterns, monitor for drift, and trigger alerts when anomalous behavior is detected in production.
AI application security requires understanding both sides of the equation β the attack techniques that target AI systems and the engineering decisions that create exploitable gaps. Our team brings 15 years of security practice together with deep hands-on AI development experience.
Most AI security gaps are discovered by attackers, not teams. Get ahead with a professional LLM security assessment before your application is targeted.
Book an AI Security Assessment β