This document outlines the security considerations, potential vulnerabilities, and best practices when using the @arcaelas/agent library.
It is important to note that this tool relies entirely on third-party APIs (OpenAI) and proprietary utilities from Arcaelas Insiders, making it inherently subject to external vulnerabilities that are beyond the direct control of the library’s developers.
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Sensitive Data Leakage: Data sent during conversations with the agent may be processed and stored by OpenAI servers, posing a potential risk of confidential information leakage.
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Malicious or Inaccurate Responses: LLMs may generate misleading, inaccurate, or potentially harmful responses if manipulated through prompt injection attacks.
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Dependency on External Availability: Any disruption in OpenAI services will directly impact the functionality of agents built with this library.
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API Key Exposure: Improper configuration or insecure handling of API keys may lead to unauthorized and potentially costly usage.
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Credential Compromise Risks: If credentials for OpenAI or Arcaelas Insiders are compromised, an attacker could abuse resources or access historical conversation data.
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Unsafe Code Execution: Tools implementing code execution, file system access, or external service connections can be exploited if not properly secured.
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Malicious Parameter Injection: Lack of validation and sanitization of tool parameters can lead to injection attacks.
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Unauthorized Access to Functionality: Without proper authentication and authorization, tools may be misused by unauthorized entities.
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Destructive Automated Actions: Automating actions based on LLM responses without human verification may lead to destructive or undesired operations.
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Infinite Loops and Resource Exhaustion: Although the agent has a 6-cycle limit, poor configuration could result in execution loops that exhaust system resources.
- Strictly validate all parameters received by agent tools.
- Sanitize all outputs generated by the model before processing or displaying them.
- Set clear limits on the size and content of inputs and outputs.
- Maintain detailed logs of all agent interactions, especially those involving tools with access to critical systems.
- Implement an alerting system to detect unusual or suspicious usage patterns.
- Regularly review logs to identify potential vulnerabilities or exploitation attempts.
- Never hardcode API keys in source code.
- Use environment variables or secure secret management systems.
- Rotate credentials regularly and follow the principle of least privilege.
- Use tokens with limited scope and expiration where possible.
- Keep the library updated with the latest security patches.
- Integrate security checks into your CI/CD pipeline.
- Conduct regular security audits on code and dependencies.
- Consider implementing throttling mechanisms to prevent abuse.
If you discover a security vulnerability in @arcaelas/agent, please report it immediately via the following channels:
- Email: community@arcaelas.com with the subject “[SECURITY] Vulnerability in @arcaelas/agent”
- GitHub: Open a confidential issue at https://github.com/arcaelas/agent/security/advisories/new
- Acknowledgment: You will receive confirmation within 24 hours of reporting.
- Initial Assessment: The security team will assess the reported issue within 72 hours.
- Mitigation Plan: A plan will be created to fix the issue, and you will be informed of the expected timeline.
- Patch and Release: Critical vulnerabilities will be addressed with top priority, with a target of releasing a fix within 7 days.
We kindly request that you:
- Provide sufficient detail to reproduce and address the vulnerability.
- Allow reasonable time for a fix before disclosing publicly.
- Do not exploit the vulnerability to access unauthorized data or disrupt the service.
It is the responsibility of users of @arcaelas/agent to implement secure and prudent practices in their deployments. This includes:
- Carefully reviewing all custom tools before integration.
- Keeping dependencies and the library itself up to date.
- Implementing additional layers of validation for critical inputs and outputs.
- Defining strict boundaries for the agent’s capabilities.
- Never blindly trusting LLM-generated responses when critical systems are involved—always verify.
This security policy will be updated periodically to address emerging risks and improve best practices. Last updated: 2025-07-17