Secure the intelligence layer.
LLM applications introduce a class of risk your existing tooling was never built to catch. We test it, fix it and keep it tested — from chatbots to autonomous agents.
A different kind of attack surface.
An LLM follows instructions from anywhere in its context. That single property redefines the perimeter: anything the model reads — a prompt, a retrieved document, a web page, a tool response — is an input channel an attacker can try to write to.
There is no patch for a probabilistic system, and “fix the prompt” does not scale. Durable security comes from architecture: privilege separation, validated outputs, layered guardrails and monitored behavior.
We test against the risk families of the OWASP Top 10 for LLM Applications (2026 edition) and the OWASP Top 10 for Agentic Applications, mapped to MITRE ATLAS and to your stack.
AI Red Teaming & Prompt Injection Testing
Your application, its prompts, its retrieval corpus and its tools — attacked the way a real adversary would, then documented the way an engineering team needs.
What we do
- Attack library tuned to your domain, plus manual adversarial creativity
- Direct and indirect injection — through documents, emails and web content your system reads
- Multi-turn manipulation, encoded payloads, jailbreak families, language switching
- Tool and agent abuse paths: what can one steered step reach?
- Exfiltration channels: generations, logs, citations, embeddings
Deliverables
- Prioritized findings with reproductions and severity
- Fix recommendations for every finding
- Executive summary leadership can act on
- A retest after fixes — included
LLM Application Security Review
Architecture and code review of the whole AI path — before launch or after, for a system you inherited or one you are about to ship.
What we do
- Context assembly: what flows into prompts, from where, with what trust
- Prompt and template hygiene; secrets living in system prompts
- RAG pipeline review: ingestion, access control, chunk exposure
- Tool and function interfaces: schemas, validation, blast radius
- Output handling: rendering, downstream execution, sensitive logging
Deliverables
- A threat model document for your system
- Architecture findings with severity and effort estimates
- Code-level issues in the integration layer
- A 90-day remediation roadmap
Guardrails Architecture & Deployment
Controls layered so a single bypass never becomes a breach — and measured, so you know what they actually stop.
What we do
- Input classification and injection detection
- Output filtering: disclosure, policy and tone enforcement
- Rate limits, budgets and per-user consumption caps
- Human-in-the-loop approval for high-impact actions
- An evaluation harness that measures false positives and bypass rates — continuously
Deliverables
- A guardrail reference architecture for your stack
- Deployed and evaluated guardrail configuration
- A regression suite wired into your CI
Agentic AI Security
Agents act. Security must bound what an action can cost — in data, money and trust — before autonomy is granted.
What we do
- Identity and least privilege per agent, per tool
- Constrained tool schemas — no free-form shell or SQL where a narrow call suffices
- Sandboxing and egress control for agent workloads
- Review of MCP servers, plugins and integrations before they join the toolbelt
- A full audit trail of autonomous actions; kill switches and spend caps
Deliverables
- An agent permission model you can enforce
- A tool and integration review report
- An incident playbook for autonomous systems
Retrieval Security & Poisoning Defense
What your model retrieves, an attacker may have planted. Retrieval-augmented generation moves your data boundary into the model’s context — we secure it there.
What we do
- Corpus provenance and ingestion hygiene: where chunks come from, who vouches for them
- Access control at chunk level — what a user may see is what the model may quote
- Poisoning detection and a takedown process
- Citation and provenance checks on generated claims
- Vector store hardening and metadata discipline
Deliverables
- A retrieval threat model for your corpus
- Ingestion and provenance controls
- Chunk-level access design, aligned with source permissions
AI Governance & Compliance Readiness
Regulation arrived. The EU AI Act, ISO/IEC 42001 and the NIST AI RMF now shape what demonstrable AI security looks like — we make you ready without drowning you.
What we do
- EU AI Act readiness: risk classification, provider and deployer duties, documentation
- ISO/IEC 42001 AI management system design
- NIST AI RMF mapping: govern, map, measure, manage
- Policies, model cards and evaluation evidence
- Incident response plans that include AI-specific scenarios
Deliverables
- A gap assessment against the frameworks that apply to you
- A prioritized compliance roadmap
- Evidence templates your auditors will accept
Start with a two-week assessment.
One sprint: threat model, red team, prioritized findings, fix plan. Enough to know where you stand — and what to do next.
Request an assessment