Find the flaws in your LLM
before an attacker does
Systematic offensive assessment of AI systems — OWASP LLM Top 10, adversarial prompt engineering, and infrastructure audit. Reproducible evidence, AI-VRM classification, and prioritized remediation plan.
From reconnaissance to risk classification
Each phase produces evidence. Each piece of evidence feeds the next — no rework, no assumptions.
Map the model's surface
Endpoints, system prompts, plugins, RAG, and data flow — documented before the first test.
surface_map.jsonTest the ten categories
Prompt injection, output handling, data poisoning, model DoS — systematic and reproducible coverage.
findings.md · 10/10Audit the stack that supports the model
Gateways, model serving, vector DBs, secrets, and isolation — where most critical flaws live.
stack_audit.logTranslate findings into decisions
Severity, exploitability, and business impact in a matrix that prioritizes what to fix first.
risk_matrix.csvOWASP LLM Top 10 — tested, evidenced, prioritized
Each category is exercised with real techniques observed in 2025–26 incidents, with reproducible PoC and AI-VRM risk classification.
Prompt Injection
Direct and indirect attacks that manipulate behavior, bypass guardrails, or trigger unintended actions.
Insecure Output Handling
Validation flaws leading to XSS, SSRF, code execution, or escalation in downstream systems.
Training Poisoning
Manipulation of pre-training, fine-tuning, or embedding data to introduce biases or backdoors.
Model Denial of Service
Intensive operations that degrade performance, inflate costs, or disrupt service.
Supply Chain
Pre-trained models, datasets, plugins, and third-party extensions that introduce vulnerabilities.
Sensitive Data Disclosure
Exposure of PII, secrets, or system prompts via responses or side channels.
Insecure Plugin Design
Tool integrations that allow unauthorized actions or code execution.
Excessive Agency
Systems with permissions or autonomy beyond the intended scope — exploitable for arbitrary actions.
Overreliance
Trusting LLM output without validation — misinformation, vulnerabilities, incorrect decisions.
Model Theft
Unauthorized extraction, replication, or exfiltration of proprietary models and weights.
What you receive at the end
No generic report. Every deliverable is addressed to the right audience — from the board to the engineer.
Board summary
Business impact analysis, top risks, and financial exposure — in 2 pages, C-suite language.
board_summary.pdfFindings with PoC
Each vulnerability with reproducible proof-of-concept, request/response logs, and CVSS+AI-VRM classification.
findings_full.mdAI-VRM Matrix
Severity × exploitability × impact, with P0/P1/P2 prioritization and suggested SLA per category.
risk_matrix.csv30/60/90 Plan
Concrete actions per finding, with owner, effort, and window. Includes a readout workshop with your team.
remediation_plan.xlsxTest your AI systems before attackers do
Schedule an AI/LLM pentest and get a full view of your attack surface — from prompt injection to model theft.
Request AI pentest