What is an LLM vulnerability scanner?

An LLM vulnerability scanner sends batteries of adversarial probes — jailbreaks, prompt injections, PII extraction attempts, harmful-content requests — at a target LLM and grades the responses. The output is a vulnerability + optimization report with per-probe evidence, severity, and a prioritized fix list.

Which LLMs can I scan with FilterPrompt?

OpenAI, Anthropic, Google Gemini, Azure OpenAI, plus any OpenAI-compatible endpoint — Ollama, Groq, Mistral, Together AI, OpenRouter, Perplexity, Hugging Face, vLLM, or your own custom endpoint. Bring your own keys per tenant.

What kinds of vulnerabilities does FilterPrompt test for?

Jailbreaks (DAN, role hijack, translation smuggling), direct and indirect prompt injection, system-prompt extraction, harmful-content compliance, PII / secret leakage, bias & fairness, RAG poisoning, agent/tool abuse, output quality, and robustness — categories map to the OWASP LLM Top 10.

How are probes graded?

Each probe declares an evaluator: regex match, refusal-check, contains-check, or an AI judge (Gemini 3 Flash). Pass/fail comes with severity, category, the exact prompt sent, the model's full response, and the evaluator's reason — fully auditable.

How much does a scan cost?

1 credit per probe executed. New accounts get 1 welcome credit on signup. Pay-as-you-go credit packs after that — credits never expire. Connecting LLMs and creating tenants is free.

Generative AI Security — The 2026 CISO Guide

Guide · 2026-04-04 · 13 min read · FilterPrompt Security Team

How to secure generative AI end-to-end — model, prompts, tools, data, and supply chain — with the vendors and frameworks that matter in 2026.

Generative AI is the fastest-adopted enterprise technology in history — and the fastest-attacked. CISOs searching for 'generative ai security', 'gen ai security', or 'genai security' typically want the same thing: a defensible answer to 'how do we protect our GenAI features?' that they can present to a board or auditor. This guide gives you that answer.

The 4 controls every GenAI program needs

AI firewall — inline prompt/response inspection with blocking.
AI vulnerability scanner — offline adversarial probes on every model change.
AI governance — inventory of models/prompts, policy, compliance mapping.
Human review — approval workflows for high-risk agent actions.

Framework alignment

Threat model — what attackers actually do

Extract system prompts to understand model constraints.
Inject instructions via user input or indirect channels.
Poison RAG stores with instructions inside retrievable docs.
Trigger unauthorized tool calls via agent injection.
Exfiltrate PII, secrets, or proprietary training data.
Cause model DoS with expensive generations.

Reference architecture

App → Firewall → Model → Firewall → App, with all traffic mirrored to a governance store. Scanner runs on a schedule against the model endpoint. High-risk agent actions route through a human-approval queue. This architecture is provider-agnostic — works with OpenAI, Anthropic, Bedrock, Vertex, or self-hosted.

Budget benchmark

In 2026, mature GenAI programs spend 3–5% of AI infrastructure budget on GenAI security controls. Below 2%, breaches become inevitable; above 8%, spend is inefficient and better redirected to governance headcount.

Vendor landscape 2026

The 90-day rollout plan

Days 1–14: Inventory every GenAI feature in the company ('shadow AI' scan).
Days 15–30: Baseline scan against OWASP LLM Top 10 with FilterPrompt Scanner.
Days 31–60: Deploy AI firewall on top-3 highest-risk endpoints.
Days 61–90: Add governance + human-review workflows; establish monthly scan cadence.