Your AI is an attack surface.
SentinelSec tests models, applications, agents, APIs, data pipelines and RAG systems against real-world attacks — the ones that actually get used, not the ones that read well in a whitepaper.
If you ship AI features, your enterprise buyers have already started asking how you secure them. Most teams have no answer yet.
Why this matters now
AI moved into production faster than security did. The result is a category of exposure most startups have never tested for — and that security questionnaires have started asking about.
The attack surface is new
Prompt injection, training-data poisoning, model extraction and agent tool-abuse do not appear in a traditional application penetration test. A clean web pentest tells you nothing about them.
Buyers are already asking
Enterprise security reviews now include AI-specific questions: what models you use, what data reaches them, what a prompt can make them do. Vague answers stall deals.
The blast radius is your data
An AI feature usually sits closer to production data than anything else you ship. A single injection flaw can turn a chatbot into an exfiltration path.
What we test
AI Red Teaming
Adversarial testing of your full AI stack, run the way a motivated attacker would approach it rather than as a checklist.
LLM Penetration Testing
Prompt injection, jailbreaks, system-prompt extraction, output handling and data leakage across your model integrations.
AI Agent Security
Agents that call tools, browse or execute code. We test what happens when an attacker controls their input and abuses their permissions.
RAG & Pipeline Security
Retrieval systems, embeddings and vector stores — including indirect injection through poisoned documents and cross-tenant retrieval leakage.
Adversarial ML
Data poisoning, model extraction and evasion against your training and inference paths.
AI Threat Modeling
Map the trust boundaries in your AI architecture before testing, so results are about your design and not generic findings.
AI Governance
Acceptable-use policies, model inventory, vendor review and the documentation auditors and customers ask to see.
AI Risk Assessment
A prioritized view of where your AI exposure actually is, sequenced by likelihood and business impact.
How an engagement runs
Findings arrive as reproducible tickets your engineers can act on — every one with the exact input that triggers it.
- Threat model
- →
- Test
- →
- Reproduce
- →
- Remediate
- →
- Retest
- →
- Report you can share
Our toolkit
A mix of proprietary and open-source tooling, chosen per engagement rather than run as a fixed scan.
Specialized AI testing tooling
Custom harnesses for prompt injection, data poisoning and model evasion.
Compliance auditing
Automated checks against GDPR, CCPA and emerging AI frameworks.
Threat simulation libraries
Pre-built adversarial scenarios that shorten time to first finding.
Explainability analyzers
Tools that surface where model decisions can be steered or exploited.
Expert-led, not tool-generated
Our team has built and broken security programs at Cigital, Intuit, PlayStation and Altimetrik. That background matters here: AI security is still a field where tooling produces noise and judgment produces findings.
We work alongside your engineers rather than delivering a PDF and leaving. Every finding comes with the reproduction steps, the fix, and a retest once you have shipped it.
Ready to secure your AI?
Tell us what you have shipped and what your customers are asking about. We will tell you what to test first.
Engagements are scoped to your stack — no fixed-scan packages.