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LLM Security Assessment
Evaluate the security of large language model deployments for prompt injection, data poisoning, model theft, and output manipulation vulnerabilities.
Model-Specific Analysis
We map your LLM architecture, its API endpoints, underlying system prompts, and integrated data sources.
What We Cover
Comprehensive Testing Framework
Our tests align with the latest standards and cover the entire attack surface of the LLM.
Direct Prompt Injection
Neutralize attacks where the user directly sends disguised malicious instructions to force the LLM to ignore its original system prompts.
Indirect Injections
Prevent attackers from injecting malicious payloads via third-party data sources such as PDF files, parsed web pages, or database records.
Uncontrolled Code Execution
Secure workflows where LLM agents are authorized to generate and execute code (such as built-in Python interpreters) to prevent host server compromise.
Need Immediate Security Assistance?
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