AI Security and Threat Assessment

The OWASP Top 10 for Large Language Model Applications identifies the most critical security risks in LLM deployments. The 2025 list reflects real world attack patterns that have matured rapidly as organizations rushed to deploy generative AI. Prompt injection remains the top threat, where attackers manipulate model behavior through crafted inputs that override system instructions. This vulnerability is particularly dangerous because it can chain into other exploits.

Sensitive information disclosure ranks high because LLMs can leak training data, system prompts, and context window contents through carefully constructed queries. Supply chain vulnerabilities target the models themselves, their training pipelines, plugins, and the libraries that connect them to your infrastructure. Insecure output handling occurs when applications trust LLM responses without validation, creating paths to cross site scripting, server side request forgery, and privilege escalation.

Our assessment methodology goes beyond a checklist review. We test each vulnerability category against your specific implementation, your models, your prompts, your integrations, and your data flows. The findings report includes actual exploitation scenarios relevant to your environment, not generic descriptions, along with prioritized remediation steps your engineering team can act on immediately.

AI red teaming applies adversarial thinking to your AI systems. Unlike traditional penetration testing, AI red teaming targets the model layer itself through prompt injection attacks that bypass guardrails, data extraction techniques that pull sensitive information from model responses, and model poisoning scenarios that test the integrity of your training and fine tuning pipelines.

The EU AI Act explicitly requires adversarial testing for high risk AI systems. Article 9 mandates that providers conduct testing designed to identify vulnerabilities, including attempts to circumvent safety measures. Even if regulation doesn't apply to your organization today, red teaming remains the most effective way to understand how your AI systems actually behave under hostile conditions rather than how you hope they behave.

Our approach combines automated fuzzing tools with manual testing by specialists who understand both the security and machine learning sides of the problem. We test jailbreak resistance using published and proprietary prompt libraries, evaluate whether your models can be coerced into producing harmful or unauthorized outputs, and probe for information leakage across session boundaries. Every finding comes with a detailed attack narrative so your team understands exactly how the vulnerability works and how to close it.

Your AI supply chain extends far beyond the model itself. It includes the foundation model provider, the training data sources, fine tuning datasets, embedding models, vector databases, plugin ecosystems, and every library in your inference pipeline. A compromise at any point in that chain can undermine the security of your entire AI deployment.

We build an AI Bill of Materials for every AI system in your environment. Similar to a software SBOM, an AI BOM catalogs every component, its provenance, version, licensing, and known vulnerabilities. This inventory becomes the foundation for ongoing supply chain risk management, giving you visibility into exactly what you depend on and where your exposure lives.

Third party model risks deserve special attention. When you use a hosted model through an API, you inherit that provider's security posture, data handling practices, and operational reliability. When you download open weights models, you take on risks around model provenance, potential backdoors, and the security of the distribution channel itself. We assess these risks using controls aligned with the Coalition for Secure AI (CoSAI) framework and help you build vendor management practices that address the unique challenges AI dependencies create.

A point in time security assessment tells you where you stand today. AI systems drift. Models degrade as the world changes around them. Bias emerges gradually as user populations shift. New attack techniques surface weekly. Without continuous monitoring, your security posture erodes faster than you realize.

Our monitoring approach tracks three dimensions simultaneously. Safety metrics catch harmful outputs, policy violations, and guardrail failures. Quality metrics detect performance degradation, hallucination rate changes, and accuracy drift that signals something has shifted in the model's behavior or the data it processes. Cost metrics flag unexpected usage spikes that could indicate abuse, prompt injection campaigns, or resource exhaustion attacks.

When monitoring detects an anomaly, your team needs to know what to do next. We build incident response playbooks for AI specific failure modes. A traditional runbook won't help when your customer facing chatbot starts producing toxic content or when an adversary finds a way to extract your system prompt. These playbooks define clear escalation paths, containment actions like model rollback or traffic diversion, and investigation procedures tailored to AI incidents. Quarterly posture reviews tie it all together, analyzing trends and updating your security strategy as the threat landscape evolves.