We employ a hybrid approach that combines automation, adversarial simulation, and manual business logic testing.
We map your AI ecosystem against frameworks like OWASP LLM Top 10 and MITRE ATLAS, identifying realistic attack paths. This ensures we’re not only testing for known flaws, but also probing for risks aligned with adversary tactics.
We simulate the methods attackers actually use to compromise AI systems:
We test whether malicious prompts — hidden in user input, documents, or multi-turn conversations — can override safety guardrails, disclose system prompts, or trigger policy violations.
We simulate query-based theft of your model’s intellectual property, including attempts to confirm training data membership, reconstruct embeddings, or regenerate sensitive training records from outputs.
We craft imperceptible perturbations, homoglyph substitutions, and Unicode payloads that bypass filters. We also use gradient-based adversarial examples to test whether your model misclassifies content under subtle manipulation.
We evaluate the end-to-end ML pipeline — from data collection and labeling to deployment. We look for data poisoning risks, label-flipping vulnerabilities, and insecure use of third-party dependencies such as unverified checkpoints or open-source libraries.
We assess the infrastructure that hosts and serves your models. This includes fuzzing inference APIs for error leakage, validating authentication and authorization, reviewing secrets management practices, and testing rate-limiting to prevent abuse or resource exhaustion.
We test if internal system instructions — which control model behavior — can be leaked to users, exposing sensitive logic or controls.
We construct multi-stage attacks that string together malicious prompts, bypassing simple filters to force unsafe actions.
We employ sophisticated jailbreaks to induce the model to generate content that violates compliance or moderation rules, thereby exposing reputational and legal risks.
We simulate insertion of malicious data into your training pipeline, creating hidden backdoors or causing targeted performance degradation.
We test if attackers can manipulate labels during training, leading to models that misclassify critical inputs in production.
We evaluate pre-trained models, libraries, and packages from registries for known vulnerabilities or malicious code that could compromise your AI stack.
We test if attackers can confirm whether sensitive records were part of your training data — exposing privacy and compliance violations.
We attempt to regenerate training samples directly from model outputs, potentially exposing personal or proprietary data.
We assess whether queries can be used to approximate or replicate your proprietary model’s weights or architecture, leading to IP theft.
We benchmark model responses across demographic groups to quantify and document fairness violations. This reduces reputational risk and regulatory exposure.
We assess whether your monitoring can detect changes in model behavior over time, ensuring attackers can’t exploit unnoticed drift to reduce accuracy or reliability.
Attackers introduce poisoned samples into an open data source you rely on. These samples contain hidden triggers that remain dormant until the model is deployed.
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As retraining occurs, the poisoned data slowly alters model logic. Because drift detection is not tuned for adversarial shifts, the manipulation goes unnoticed.
The attacker engages your chatbot and uses a multi-turn injection chain to override safety filters and extract system prompts.
With repeated queries, the attacker reconstructs embeddings and regenerates fragments of sensitive training data, compromising both IP and personal information.
The stolen model is cloned and resold. Sensitive data surfaces on underground forums. Your enterprise faces regulatory fines, reputational loss, and IP theft — all from an attack chain that exploited overlooked AI weaknesses.
At the end of the engagement, you receive a complete package that delivers both technical depth and business clarity:
Severity-ranked vulnerabilities, mapped to adversary TTPs, with proof-of-concept exploits demonstrating impact.
Prioritized fixes tailored to your infrastructure and business environment, including compensating controls where redesign is costly.
A high-level overview connecting technical risks to compliance, legal, and business outcomes.
A high-level overview connecting technical risks to compliance, legal, and business outcomes.
Ongoing benchmarking and drift monitoring to keep AI secure against new adversarial techniques.
Offensive security professionals with hands-on adversarial ML expertise.
Combining automation, red-team tradecraft, and contextual business logic testing.
Findings mapped to OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, ISO/IEC AI Security, and EU AI Act readiness.
Data ingestion, training pipelines, deployment, inference APIs, and monitoring systems.
From initial scoping to remediation support and retesting, we partner with you to keep AI resilient over time.