AI Security Assessment

AI Systems Are the New Attack Surface

Organizations are deploying AI and machine learning models at unprecedented speed — from customer-facing chatbots and recommendation engines to internal fraud detection systems and automated decision-making pipelines. Each of these systems introduces novel attack vectors that traditional security assessments do not cover. Adversaries are already exploiting these gaps: poisoning training data to manipulate model behavior, crafting adversarial inputs that bypass classifiers, extracting proprietary models through carefully designed queries, and using prompt injection to override safety controls in large language models.

Mjolnir Security's AI Security Assessment service applies offensive security methodology to your AI/ML systems. We think like attackers to find vulnerabilities before they are exploited in production, and we provide actionable remediation guidance that your data science and engineering teams can implement immediately.

Our assessment methodology is grounded in the OWASP Top 10 for LLM Applications, MITRE ATLAS (Adversarial Threat Landscape for AI Systems), and NIST AI Risk Management Framework. We combine automated tooling with manual expert analysis to deliver comprehensive coverage across all AI attack categories.

Assessment Scope

Adversarial Robustness Testing

We craft adversarial inputs designed to cause misclassification, bypass content filters, or trigger unintended model behavior. For image classifiers, this includes perturbation attacks (FGSM, PGD, C&W) and patch-based attacks. For NLP models, we test with adversarial text generation, homoglyph substitution, and semantic-preserving perturbations:

• White-box attacks against models where architecture and weights are accessible
• Black-box attacks using only query access, simulating an external attacker
• Transferability testing to assess whether attacks on surrogate models transfer to production
• Robustness benchmarking against established adversarial example datasets

Data Poisoning Detection

We analyze training data pipelines to identify vulnerabilities that could allow an attacker to inject malicious samples into the training set. Poisoned data can create backdoors in models, bias outputs toward attacker-desired outcomes, or degrade model performance on targeted input classes:

• Training data provenance and supply chain analysis
• Statistical anomaly detection in training datasets
• Backdoor detection using spectral signatures and activation clustering
• Label-flipping and clean-label attack simulation

Prompt Injection & LLM Security

For organizations deploying large language models, we conduct comprehensive prompt injection testing including direct injection, indirect injection through retrieved documents, and jailbreak techniques. We test system prompt extraction, tool-use abuse, and data exfiltration through model outputs.

Model Extraction & Inference Attacks

We assess whether attackers can steal your proprietary model through systematic querying (model extraction), determine whether specific data points were in the training set (membership inference), or reconstruct sensitive training data (model inversion). These attacks can compromise both intellectual property and data privacy.

Model Governance Review

Beyond technical attacks, we review model governance practices including version control, access controls, deployment pipelines, monitoring for drift and degradation, and incident response procedures for AI-specific failures.

Deliverables

Every assessment concludes with a detailed report containing vulnerability findings, proof-of-concept demonstrations, risk ratings aligned to business impact, and prioritized remediation recommendations. We provide an executive summary for leadership and a technical appendix with reproduction steps for your engineering team.