FOINIK

AI Security Testing Platform

Enterprise-grade penetration testing for Large Language Models. Powered by the MYNDRA methodology to identify and exploit AI vulnerabilities before attackers do.

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Live Attack Simulation

$ foinik --target api.enterprise-llm.com --mode aggressive

[*] Initializing FOINIK Attack Engine v0.1
[*] Loading MYNDRA methodology...
[+] Target identified: GPT-4-Enterprise
[*] Executing reconnaissance phase...
[+] Model fingerprinting complete
[*] Testing 147 attack vectors...

[!] Prompt injection vulnerability detected
[!] System prompt extraction possible
[!] CRITICAL: Jailbreak successful with DAN-11.0
[!] Memory poisoning attack viable

[+] Penetration test complete
[*] Vulnerabilities found: 4 CRITICAL, 7 HIGH, 12 MEDIUM
[*] Time elapsed: 3.7 seconds
[+] Report generated: ./reports/enterprise-llm-audit.pdf

Comprehensive AI Security Testing

Model Fingerprinting

Instantly identify AI model versions, configurations, and potential attack surfaces through advanced reconnaissance.

Prompt Injection Testing

Automated testing of 100+ prompt injection techniques to bypass guardrails and security measures.

Memory & RAG Attacks

Test resilience against context poisoning, false memory injection, and vector database manipulation.

Jailbreak Automation

Deploy evolving jailbreak techniques that adapt to model defenses in real-time.

MITRE ATLAS Mapping

Complete coverage of MITRE ATLAS framework with detailed compliance reporting.

Attack RNA™ Technology

Self-learning attack patterns that evolve and share successful exploits across assessments.

The MYNDRA Methodology

M

Mapping

Comprehensive discovery of AI attack surface and entry points

D

Discovery

Deep reconnaissance of model capabilities and weaknesses

T

Testing

Systematic exploitation using adaptive attack chains

R

Reporting

Executive and technical reports with remediation guidance

147+
Attack Vectors
3.7s
Average Scan Time
15+
LLM Models Supported

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