Model Inversion Defense

We simulate attacks aimed at reconstructing sensitive training data from model outputs, ensuring your proprietary IP and user data remain private.

RAG Pipeline Auditing

Testing the security of your vector databases and document retrieval logic to prevent unauthorized access to restricted organizational knowledge.

Evasion Attack Mitigation

Stress-testing classifiers and computer vision models against adversarial perturbations designed to fool the AI into incorrect decision-making.

Adversarial Evasion in Credit Risk Scoring

The Vulnerability: In the financial sector, proprietary ML models for credit underwriting are susceptible to “evasion attacks.” Sophisticated actors can utilize gradient-based optimization to identify the minimal “perturbations” in application data—often imperceptible to human auditors—that force a model to flip its decision from a high-risk denial to a low-risk approval.

The Sabalynx Solution: Our team conducts “White-Box” and “Grey-Box” penetration testing to map the decision boundaries of your neural networks. We simulate Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks to stress-test your feature engineering pipeline, ensuring that your risk assessment logic remains robust against synthetic data manipulation designed to bypass capital reserve compliance.

Inference Attacks on Federated Diagnostics

The Vulnerability: Healthcare providers often utilize Federated Learning (FL) to train diagnostic models across multiple institutions without sharing raw patient data. However, the model gradients themselves can leak highly sensitive Protected Health Information (PHI). Through “Model Inversion” or “Membership Inference” attacks, an adversary can reconstruct individual patient records just by analyzing the differential updates to the global model.

The Sabalynx Solution: We deploy advanced cryptographic auditing and differential privacy simulations to quantify the leakage risk in your FL architecture. By attempting to re-identify training samples from public API responses, we validate your noise-injection strategies and secure your healthcare IP against malicious data extraction that could lead to massive HIPAA violations.

Indirect Prompt Injection in Enterprise RAG

The Vulnerability: Retrieval-Augmented Generation (RAG) pipelines are the backbone of modern enterprise AI agents. However, they introduce a critical “Indirect Prompt Injection” vector. An attacker can place poisoned documents on a website or in a shared drive that your AI assistant subsequently crawls. These documents contain hidden instructions that hijack the LLM’s logic, potentially tricking the agent into exfiltrating corporate data or executing unauthorized API calls.

The Sabalynx Solution: Our red teaming engagement focuses on the interaction between the Vector Database, the Embeddings model, and the LLM Orchestrator. We simulate “Sleepy Agent” attacks and “ASCII Smuggling” techniques to determine if your system can be manipulated via external knowledge sources. We then help you implement semantic firewalls and output verification layers to neuter these injections.

Defeating AI-Driven Anomaly Detection (SIEM)

The Vulnerability: Security Operations Centers (SOCs) are increasingly reliant on AI-powered SIEM and XDR tools to detect intrusions. However, these defensive AI models often have a “blind spot”—if an attacker moves slowly and mimics the baseline statistical noise of the network (Model Poisoning), they can permanently shift the AI’s definition of “normal,” effectively blinding the system to future exfiltration attempts.

The Sabalynx Solution: We act as the adversary to test the resilience of your AI-driven cybersecurity defenses. Our consultants conduct “drift-based” attacks where we slowly introduce malicious traffic to see if your anomaly detection model adapts to the threat rather than alerting on it. This ensures your automated security stack isn’t just a “black box” that can be lulled into complacency.

Model Extraction & Intellectual Property Theft

The Vulnerability: For SaaS companies, the core value often lies in a proprietary, fine-tuned model accessible via API. Attackers can use “Model Extraction” techniques—querying your API thousands of times with strategically designed prompts—to effectively “train” a shadow model that clones your model’s performance. This results in the total loss of your competitive advantage and R&D investment.

The Sabalynx Solution: We perform comprehensive API stress-testing to identify the threshold at which your model’s logic becomes reconstructible. We evaluate your rate-limiting, output watermarking, and response-variance strategies to prevent “Knowledge Distillation” attacks. Our goal is to ensure that while your model is accessible, its underlying architecture and weights remain a proprietary secret.

Adversarial Patches in Industrial Computer Vision

The Vulnerability: In manufacturing and smart grid infrastructure, Computer Vision (CV) is used for quality control and safety monitoring. Physical “Adversarial Patches”—specific patterns printed on objects—can cause these CV models to misclassify dangerous failures as safe operations or completely ignore restricted personnel. In a critical infrastructure context, this can lead to physical sabotage or catastrophic equipment failure.

The Sabalynx Solution: We conduct hybrid physical-digital penetration tests. Our experts develop and test adversarial patterns against your production CV models to identify weaknesses in object detection and semantic segmentation. We then provide recommendations for multi-modal verification (e.g., combining visual data with thermal or acoustic sensors) to ensure your automation cannot be fooled by a simple sticker or light projection.