XC
0MRADE
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Proactively test your artificial intelligence applications, Large Language Model deployments, and agentic workflows against adversarial attacks. Validate boundaries before code hits production.
We cover the entire lifecycle of artificial intelligence infrastructure, from training datasets to live runtime endpoints.
We attempt to bypass model alignment boundaries using advanced semantic engineering, jailbreaks, and indirect injection vectors. We verify that malicious prompts cannot coerce models into leaking system instructions.
We audit dataset ingestion pipelines. We ensure public-facing feedback loops or data loaders cannot be poisoned by adversaries seeking to introduce backdoor trigger behaviors into your fine-tuned weights.
Our researchers test if private user records or proprietary training data can be reconstructed by querying the model API endpoints. We prevent leakage of private personal data (PII).
We audit agentic tools that execute actions on behalf of the user. We ensure retrieval-augmented generation (RAG) datasets do not inject untrusted documents that hijack tool arguments or execute dangerous code.
We check if API interfaces are vulnerable to resource-exhaustion attacks. We test if high-complexity queries can be batch-submitted to inflate server compute costs or trigger service outages.
We validate third-party base models, fine-tuned weights, and pipeline libraries. We search for security flaws inside serialization formats (e.g., Pickle) and check dependency vulnerabilities.
Why model-based software applications require different validation techniques.
| Attack Vector | Legacy Infrastructure Pentest | XC0MRADE AI Red Teaming |
|---|---|---|
| Prompt Vulnerability | Only checks host system configurations (e.g. server ports). | Actively manipulates prompts using semantic fuzzing to leak model instructions. |
| RAG Data Injection | Scans files for viruses, but cannot check context hijacking. | Injects toxic payloads inside vector databases to test retrieval sanitization. |
| Model Output Auditing | Completely blind to outputs generated by natural language. | Validates filters to prevent model jailbreak outputs and toxic telemetry. |
We follow a structured framework to map and exploit AI deployment pipelines.
Share access to LLM model endpoints, wrapper APIs, and pipeline orchestrations (LangChain).
We trigger automated test beds to verify system prompts and safety filters.
Our vetted security researchers execute complex multi-step exploits to override model instructions.
Vulnerabilities are validated, ranked by severity, and pushed to your secure dashboard.
Find answers to standard security questions regarding AI red teaming and model protection.
AI Red Teaming is a specialized security methodology that simulates real-world adversarial attacks against artificial intelligence networks, model weight pipelines, and software wrappers. We look for software vulnerabilities, data leaks, and behavioral alignment failures.
We secure pipelines built with OpenAI API, Anthropic, Hugging Face, LangChain, LlamaIndex, TensorFlow, PyTorch, and custom enterprise deployments hosted on AWS Bedrock or GCP Vertex AI.
Yes. While our primary focus is security vulnerabilities (data extraction, command injection, auth bypasses), we evaluate alignment boundaries to ensure models do not generate toxic content, illegal instructions, or severe hallucinations.
Our global researcher community is active on the bleeding edge of AI research. We map our findings to modern benchmarks, including the OWASP Top 10 for LLM Applications and MITRE ATLAS framework.
Validate model boundaries and protect your intellectual property before deploying to the public web.