AI Vulnerability Detection

CloudAEye identifies threats specific to LLM-based workflows, such as prompt injections and API misuse. It also detects vulnerabilities in hybrid RAG systems, ensuring secure retrievals and outputs across AI pipelines.

AI Code Stability & Security

Scans code for logic flaws and misconfigurations in both RAG workflows and traditional application layers. Ensures LLMs are deployed securely and integrates safely with other systems, minimizing risks like data exposure.

Code Reviews

Automates reviews of AI and non-AI codebases, highlighting vulnerabilities such as insecure data handling in RAG workflows or improper encryption in legacy applications. Maintains consistent security standards across all environments.

Bug Categorization

Categorizes security bugs from AI pipelines (e.g., RAG misconfigurations, token misuse) alongside traditional vulnerabilities like SQL injections and authentication errors. Simplifies prioritization across mixed systems.

Automated Test Analysis and Categorization

Surfaces security-related test failures, such as misaligned retrieval logic in RAG systems or improper access control in APIs. Groups issues for faster resolution, improving security across AI and standard systems.

Pull Request (PR) Descriptions

Creates PR descriptions that emphasize security-critical changes, like updates to LLM prompts, RAG retrieval logic, or backend authentication mechanisms. Helps reviewers focus on high-risk areas.

JIRA Integration

Generates actionable guides for resolving security flaws in RAGs, LLMs, or standard application layers. Accelerates issue resolution while maintaining compliance and reducing vulnerabilities.