AI Security Assessment Built on Real Attack Methodology

Your AI System Has Attack Surfaces Your Security Team Has Never Tested

Traditional penetration testing covers your APIs, your infrastructure, your authentication flows. It does not cover whether an attacker can extract your fine-tuned model through 50,000 carefully structured queries. It does not test whether a poisoned LoRA adapter in your HuggingFace dependency chain introduced a backdoor three months ago. It does not evaluate whether your RAG pipeline will execute instructions embedded in a retrieved document. These are the attack paths that actually compromise AI systems in production, and they require assessment methodology built specifically for how AI systems fail.

Protect AI found 352,000 suspicious files across 51,700 models on HuggingFace in April 2025. AI-enabled attacks rose 89% year-over-year, with 97% of breached organizations lacking basic access controls on their AI systems. The Mercor supply chain attack in early 2026 compromised thousands of companies through a single open-source dependency. These are not theoretical risks from conference talks. They are the operational reality of deploying AI without testing it the way attackers approach it.

What We Actually Test (and What Most Assessments Miss)

We structure assessments around the MITRE ATLAS framework, which now catalogs 84 techniques across 16 tactics specifically targeting AI systems. But a framework is a map, not a test. We run the attacks.

For LLM deployments, we test indirect prompt injection through every ingestion path: RAG retrieval, tool outputs, user-uploaded documents, email content fed to agents. Direct injection gets the headlines, but Anthropic dropped its direct injection metric entirely in February 2026 because indirect injection through retrieval context is what actually breaks production systems. We test multi-turn manipulation, system prompt extraction, and the specific failure modes of whatever guardrail stack you have in place.

For model-level security, we assess extraction risk by running structured query campaigns against your API and measuring how much model behavior an attacker can replicate. We evaluate adversarial robustness with both gradient-based methods (when we have model access) and transfer-based black-box attacks (which is how real attackers operate). We audit your training pipeline for data poisoning vulnerabilities, checking both your direct training data and the upstream dependencies that feed it.

For supply chain, we trace every model artifact back to its source: pre-trained weights, fine-tuning datasets, adapter layers, serving framework versions. We check for known vulnerabilities in your ML infrastructure (PyTorch, vLLM, Triton Inference Server all had CVEs in 2025-2026) and verify that your model serialization uses safe formats. The $12 billion in losses from compromised ML models in 2025 came overwhelmingly from supply chain attacks, not direct model exploitation.

For agentic systems, we test the attack surface that OWASP's new Agentic AI Top 10 defines: goal hijacking, tool misuse, identity abuse, memory poisoning, and cascading failures across multi-agent workflows. The OpenClaw crisis in 2026, where 21,000+ instances of a 135,000-star AI agent were exposed to critical vulnerabilities, showed what happens when agents ship without this testing.

Hardening That Changes How Your System Operates

Assessment without remediation is an expensive PDF. We build the hardening controls into your system.

For inference-layer defense, we implement query anomaly detection that identifies extraction-pattern traffic (systematic input coverage, boundary probing, programmatic paraphrase sweeps) and distinguishes it from legitimate usage. We deploy input validation pipelines tuned to your specific threat model, not generic regex filters that miss semantic attacks and block legitimate queries.

For supply chain hardening, we build model verification pipelines that check artifact provenance, validate serialization formats, scan for known malicious patterns, and enforce signing requirements before any model artifact enters your deployment pipeline. We establish dependency monitoring that catches compromised upstream packages before they reach production.

For agentic system hardening, we implement privilege boundaries around tool access, output validation between agent steps, and behavioral monitoring that detects when an agent's execution pattern diverges from its expected workflow. Your SIEM was built to detect human behavior anomalies. An agent that executes 10,000 queries in sequence looks normal to those systems even when it is operating under attacker control.

When You Do Not Need This

If you are calling a managed API (OpenAI, Anthropic, Google) with no fine-tuning, no RAG, no tool use, and no sensitive data in prompts, your security risk is API key management and data handling. A standard application security review covers that. You do not need AI-specific assessment.

If your model is a simple classifier running internally with no external-facing API and no retraining pipeline, your attack surface is limited. A brief threat model review is proportionate. Full adversarial robustness testing on an internal sentiment classifier behind a firewall is spending $30,000 to protect a $500 risk.

We tell clients this because credibility matters more than revenue. The organizations that need this work know who they are: anyone with fine-tuned models, RAG pipelines processing external content, agentic systems with tool access, models in regulated industries, or AI systems making decisions with financial or safety consequences.

The Regulatory Pressure Is Real and Has Deadlines

EU AI Act enforcement begins August 2026. High-risk AI systems require documented risk management, technical robustness testing, and data governance controls. Non-compliance carries fines up to 7% of global annual turnover or EUR 35 million. NIST published its Cybersecurity Framework Profile for AI in December 2025, mapping AI-specific risks to CSF 2.0 controls. These frameworks are now showing up in procurement requirements and board-level risk reviews.

The challenge is that no single framework covers everything. MITRE ATLAS maps attack techniques. OWASP LLM Top 10 categorizes vulnerability classes. NIST AI RMF provides governance structure. ISO 42001 handles management systems. EU AI Act imposes legal obligations. We map your assessment findings to whichever frameworks your regulators, auditors, and customers require, producing evidence that satisfies compliance requirements because it comes from actual testing, not checkbox exercises.

Platform Tools vs. Custom Assessment

Automated AI security platforms (HiddenLayer, Mindgard, Giskard) run known attack patterns at scale. They are useful for continuous regression testing after an initial assessment. They are not a substitute for the initial assessment itself. A scanner does not understand your business logic, does not know which model outputs carry safety-critical consequences, and cannot evaluate whether your threat model matches your actual deployment architecture.

We use these tools where they add value. Continuous automated red teaming belongs in your CI/CD pipeline once we have established what to test for. But the attack paths that matter most in your specific system require someone who understands both the AI failure modes and your operational context to find them.

For organizations running multiple AI vendors (OpenAI, Anthropic, Google, open-source models), we assess each provider's security boundaries independently and test the integration points where data flows between them. The attack surface of a multi-vendor stack is not the sum of each vendor's risk. It is the interaction layer, where assumptions about one provider's security guarantees break down at the handoff to another.