Your AI Model Is Live.
Is It Secure?
Every AI feature you ship is a new attack surface your team has never seen before. Prompt injection, data leakage, agent hijacking — these vulnerabilities don't show up in any traditional scanner.
One breach doesn't just cost you money. It destroys the client trust you spent years building — in hours.
34%
of newly deployed LLM applications are successfully breached within their first month in production
$4.9M
average cost of an AI-related data breach in 2024 — not counting reputation damage and client churn
67%
of customers say they would stop using a product after a single AI-related security incident
What Happens When You Ship AI Without Testing It
These aren't edge cases. They are the exact scenarios we find in every LLM engagement — across startups, scale-ups, and enterprise products alike.
Client Data Leaked via Prompt
An attacker crafts an input that overrides your system prompt and extracts other users' conversation history, PII, or proprietary data — in real time.
IP and Model Stolen
Your fine-tuned model, training data, or system instructions — the competitive edge you spent months building — extracted through misconfigured APIs or output probing.
Reputation Destroyed Publicly
Your AI produces harmful, biased, or manipulated output — posted on social media before you even know it happened. The damage to brand trust cannot be undone.
Agent Hijacked System-Wide
A single compromised tool call inside an AI agent cascades through your entire pipeline — deleting data, exfiltrating files, or triggering unauthorized transactions at scale.
You built the AI feature to win more clients. Don't let it become the thing that loses them all.
Our LLM security engagement finds the vulnerabilities your dev team, your DAST tools, and your cloud provider will never catch — because they were never designed to test AI systems.
Continuous LLM Security. One Fixed Fee.
A dedicated security expert on your AI systems every month — scoped on your call, delivered in 20–30 days per cycle, with no surprise invoices.
per month · fixed fee · dedicated effort
Exact price scoped on the call based on model count, integrations, and agent complexity
12-Month Retainer
Annual commitment — cancel after year one
50% Upfront
Half the annual total paid at contract signing
Delivered in 20–30 Days
Each monthly cycle delivered on schedule
No commitment until contract · Quote within 24h · NDA before access
Why an annual retainer?
LLM security isn't a one-time checkbox. Your models evolve, your integrations change, and new attack techniques emerge every month. Continuous testing means vulnerabilities are caught between releases — not after a breach. The annual commitment lets us stay fully embedded in your security posture rather than starting from scratch each time.
Assessment Depth by Tier
Exactly what's included in each level — no ambiguity, no surprises.
Black-box
Standard
- Prompt injection attacks
- Output manipulation testing
- Adversarial input testing
- Jailbreak & bypass attempts
- External OWASP LLM checks
Gray-box
Deep Dive
- All black-box testing
- API security analysis
- Plugin security review
- Agent chain attack simulation
- Full OWASP LLM Top 10
White-box
Full Access
- All gray-box testing
- Training data poisoning analysis
- Model theft prevention
- Supply chain security
- Full model internals audit
Coverage by the Numbers
Every engagement maps findings to the two authoritative LLM security standards — so you know exactly what was tested, why it matters, and how to fix it.
OWASP Top 10 for LLMs
All 10 vulnerability classes tested and reported per engagement
Prompt Injection
Manipulating LLM behavior through crafted inputs — direct and indirect vectors
Sensitive Information Disclosure
Exposure of PII, credentials, and proprietary data via model outputs
Supply Chain Vulnerabilities
Compromised models, datasets, and third-party dependencies
Data and Model Poisoning
Malicious training data corruption and backdoor attack injection
Improper Output Handling
Insufficient validation of LLM-generated content leading to downstream exploits
Excessive Agency
Unchecked autonomous AI agent permissions executing unintended actions
System Prompt Leakage
Exposure of sensitive system prompts, configs, and internal instructions
Vector and Embedding Weaknesses
RAG-specific vulnerabilities, embedding poisoning, and vector DB data leakage
Misinformation
Hallucination, bias, and dangerous overreliance on unverified model output
Unbounded Consumption
Resource exhaustion, denial-of-wallet, and economic denial-of-service attacks
LLM Security Verification Standard (LLMSVS)
8 domain controls verified against your implementation
Secure Configuration & Maintenance
Model deployment settings, hardening, and ongoing patch posture
Model Lifecycle
Security controls across training, evaluation, deployment, and retirement
Real-Time Learning
Risks from live feedback loops, RLHF, and continuous fine-tuning pipelines
Model Memory & Storage
Persistent memory systems, vector stores, session context, and data retention risks
Secure LLM Integration
API exposure, authentication flows, and trust boundaries between services
Agents & Plugins
Tool-call permissions, agent autonomy limits, and plugin isolation controls
Dependency & Component
Third-party model libraries, SDKs, and dataset provenance verification
Monitoring & Anomaly Detection
Logging, alerting, and behavioral anomaly detection for inference-time attacks
All findings are mapped to both frameworks in the final report — with severity ratings, proof-of-concept evidence, and step-by-step remediations.
Common Questions
Do I need to give you access to the model internals?
No — black-box (Standard) requires zero access beyond the model endpoint. Gray-box (Deep Dive) adds API documentation. White-box (Full Access) requires model-level access for maximum depth. We always sign NDA before any access.
We use a third-party LLM (GPT, Claude, Gemini) — is that testable?
Yes. We test how your application integrates and instructs the model — system prompts, RAG pipelines, tool calls, data exposure — regardless of which underlying LLM you use.
How long does each monthly cycle take?
Each monthly cycle is delivered in 20–30 days. The scope for the cycle is agreed at the start of the month based on any new integrations, model changes, or carry-over findings from the previous cycle.
Your AI is live. Let's make sure it stays safe.
One breach can erase years of reputation. A monthly retainer keeps you ahead of every new attack technique — not catching up after one lands.
Book Free 15-Min CallNo card required · Response in 24h · NDA before access