Go Beyond Scanners.
Validated Attack Paths for your AI Infrastructure

We secure your LLM and RAG integrations using true offensive validation, not just static checklists. Proactively discover advanced Data Leakage and Prompt Injection vulnerabilities.

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Who is this for?

  • Companies developing AI assistants accessing enterprise documents (RAG) within internal networks.
  • MLOps teams integrating weight files or LLM models from external/open-source platforms (like Hugging Face).
  • Teams embedding Agentic AI talking to B2C users via API or Chat interfaces.

Target Threat Surface

Security scanners cannot audit ML layers. Our offensive security analysts perform full-stack AI penetration testing:

Model File Processing (pkl, gguf, onnx)
RAG & Vector Database Integrity
Prompt & Orchestration Layer (LangChain)

Our Proof-Driven Methodology

01

Scoping

Your model architecture, data connectors, and LLM endpoints are mapped out.

02

Recon & Reconaissance

We identify MFV, RCE, and Tool Misuse vulnerabilities in HuggingFace files or Langchain Agents.

03

Exploit & Proof

Manually crafted malicious prompts (jailbreaks) and MFV payloads execute code to prove absolute impact.

04

Patch & Retest

We provide technical support while your team patches critical layers, then verify the fix.

Typical Exploit Findings

  • Indirect Prompt Injection (RAG)Hidden text on a malicious website manipulating your Copilot to exfiltrate internal PII data.
  • Model File Vulnerabilities (MFV)Achieving Remote Code Execution (RCE) on backend servers due to loading a poisoned ML model file (Pickle/GGUF/ONNX).
  • Agentic API Authorization BypassCleverly prompting an LLM agent to access restricted API functions and fetch financial data.

Deliverables

Instead of scanner logs or automated PDF dumps, we deliver step-by-step reproducible Proof of Concepts (PoC), Business Impact analyses, and Remediation code snippets for developers.

$ torch.load("malicious_model.pth")
[!] EXPLOIT SUCCESS
[+] System compromised via __reduce__ overriding.
[+] Remediation: Switch to safe_globals / Hugging Face safetensors format.