Explore the beyond. Not the boundary.
Projects powered by AI to encompass Security and AI Red Teaming models to identify vulnerabilities, prevent adversarial attacks, and ensure compliance for enterprise Large Language Models (LLMs) and ML pipelines.
AI systems break differently
Prompt Injection & Jailbreak Testing
We probe instruction-following boundaries, system-prompt leakage, and adversarial inputs that cause the model to override its own constraints.
Model Poisoning & Data Integrity
We test whether fine-tuning pipelines or retrieval layers can be manipulated to introduce persistent backdoors or corrupt inference at scale.
Reasoning Vulnerability Assessment
We document where the model's logic chain can be exploited—hallucinated authority, inconsistent guardrails, and inference-time manipulation vectors.
Every engagement. Documented findings.
No assumptions about safety
Findings, not checkboxes
Scoped to your deployment
Deliverables are technical reports: specific vulnerabilities, reproduction steps, and risk-ranked remediation paths—not a compliance attestation.
Every model enters the engagement with zero inherited trust. We test as an adversary would—without advance knowledge of guardrails or safety layers.
Each engagement is scoped to the actual model architecture, data pipeline, and threat model—not a generic AI audit template applied off the shelf.
Tested under fire. Before it matters.
If your AI system hasn't been tested against adversarial inputs, you don't know its attack surface. Scoping calls happen within one business day.