Cynative: Open Source Deep Research Agent

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Cynative: Open-source deep research agent A system designed to analyze cloud environments for security vulnerabilities while minimizing risk operates by restricting its actions to read-only operations.

Overview of Cynative

A system designed to analyze cloud environments for security vulnerabilities while minimizing risk operates by restricting its actions to read-only operations. This approach prevents unintended modifications to critical infrastructure by default, ensuring that any potential threats are identified without compromising data integrity.

Security Measures and Read-Only Operations

The tool integrates with enterprise codebases, cloud platforms, and runtime environments, executing generated code within a secure sandbox. It enforces strict access controls by leveraging preconfigured policies that limit interactions to read-only functions across major cloud providers and development platforms.

Cloud Provider Integration

The agent utilizes credentials stored in the user’s local environment to interact with cloud services such as AWS, GCP, Azure, and Kubernetes clusters, both managed and self-hosted. All write operations require explicit approval, while all other actions are governed by predefined read-only roles.

Dynamic Permission Management

On AWS, this includes SecurityAudit permissions; on GCP, roles/viewer; and on Azure, Reader. Maintaining up-to-date mappings between actions and permissions is a dynamic challenge as cloud providers frequently introduce new features.

Source of Classifications

To address this, the system sources its classifications directly from cloud provider APIs, including AWS Service Reference API and SDK models, with fallback mechanisms like the community iam-dataset. These sources are refreshed periodically, typically every 24 hours, to ensure compatibility with the latest service updates.

Verification and Data Isolation

When encountering unclassified actions, the agent defaults to a read-only restriction, preventing any potential unauthorized modifications. This process involves validating new actions against existing policies using tools like AWS’s iam:SimulateCustomPolicy.

Containment and Transparency

If a new action cannot be mapped, it is automatically denied, ensuring no write operations occur. This approach prioritizes containment over absolute immunity, acknowledging that adversarial inputs could influence the model’s reasoning but limiting the impact to non-destructive outcomes.

Conclusion

The system further isolates all tool outputs and scanned data, treating them as untrusted content. It employs strict boundary controls to prevent data leakage and ensures every operation aligns with the user’s defined objectives. Findings undergo a secondary verification phase, where raw evidence is scrutinized through independent refutation checks.

A key demonstration of this process involves identifying a potential privilege-escalation vulnerability in an OIDC configuration. The agent confirms this by analyzing readable data, such as trust policies and attached permissions, without executing any write operations.

For findings requiring write access to validate, the system explicitly marks them as unverified, maintaining transparency about the limitations of its analysis. Design choices reinforce the read-only framework, including persistent audit logs that halt operations if any step fails to record.