On-prem AI, attacker-tested.
I architect sovereign, on-prem LLM systems for regulated enterprises — then red-team and defend them. Offensive security and blue-team detection, mapped to recognised frameworks, are built into every engagement. Not slides: threat models, exploit chains, detections, and deliverables you can hand to auditors.
Three disciplines, one operator
Most vendors do one of these. The value is in the overlap: someone who has put on-prem LLMs into production knows exactly where they break — and how to detect it when they do.
AI / LLM Red Teaming
Adversarial testing of LLM applications, RAG pipelines and agents — prompt injection, insecure output handling, model DoS, supply-chain and data exfiltration — structured on the OWASP LLM Top 10 and MITRE ATLAS.
Explore red teaming →Blue Team & Detection Engineering
Turning LLM observability into security telemetry: prompt-injection detection, guardrail enforcement, anomaly detection on inference, Sigma-style rules and a DFIR playbook for AI incidents — mapped to MITRE ATT&CK.
Explore blue team →Secure Sovereign AI (build)
On-prem / hybrid LLM architectures designed secure-by-design: guardrails, secrets isolation, zero data egress, auditable pipelines and observability — the reference architecture that the red and blue work plugs into.
See the architecture →OWASP LLM Top 10 — the backbone
Every AI red-team and hardening engagement is structured on the OWASP Top 10 for LLM Applications, so findings map to a framework your CISO, auditors and regulators already recognise.
| ID | Risk | What it means for an on-prem deployment |
|---|---|---|
LLM01 | Prompt Injection | Direct and indirect (via RAG documents, tools, web) instruction hijacking that overrides system intent. |
LLM02 | Insecure Output Handling | Model output trusted downstream — XSS, SSRF, SQL/command injection through un-sanitised generations. |
LLM03 | Training Data Poisoning | Tampered fine-tuning / RAG corpora that bias, backdoor or degrade the model. |
LLM04 | Model Denial of Service | Resource-exhausting prompts, context flooding and cost/latency abuse of self-hosted inference. |
LLM05 | Supply Chain Vulnerabilities | Compromised model weights, datasets, adapters and Python/CUDA dependencies. |
LLM06 | Sensitive Information Disclosure | Leakage of PII, secrets or proprietary data via responses, retrieval or logs. |
LLM07 | Insecure Plugin / Tool Design | Over-permissioned tools and agents that let a model take unsafe actions. |
LLM08 | Excessive Agency | Autonomy, permissions and functionality beyond what the use case needs. |
LLM09 | Overreliance | Unverified model output driving decisions without human or programmatic checks. |
LLM10 | Model Theft | Extraction, exfiltration or unauthorised access to self-hosted model weights. |
How an engagement runs
Scoped, evidence-driven, and safe by default. Every finding is reproducible and mapped to a framework control.
Scope & threat model
Define assets, trust boundaries and rules of engagement. Threat-model the LLM/RAG/agent stack with STRIDE and MITRE ATLAS before touching anything.
Offensive testing
Adversarial testing across the OWASP LLM Top 10 and OWASP WSTG — from prompt-injection chains to insecure output handling and model DoS — with reproducible proof of concept.
Detection & hardening
For each viable attack, design the blue-team counterpart: detection logic, guardrails, and architecture fixes. Offense and defense in the same loop.
Report & retest
Risk-rated findings, an executive summary for the board, a technical report for engineering, and a retest to confirm remediation.
Put your AI stack under adversarial pressure
An AI Security Assessment scopes your LLM attack surface against the OWASP LLM Top 10 and returns a prioritised, auditor-ready remediation plan.
Request an AI Security Assessment Responsible disclosure