AI-Infra-Guard is a cutting-edge AI Red Teaming platform by Tencent Zhuque Lab, designed to enhance security through intelligent vulnerability scanning and agent simulations. It helps organizations to identify and mitigate potential threats effectively.
claude install Tencent/AI-Infra-Guardhttps://tencent.github.io/AI-Infra-Guard/
1. **Define Scope**: Clearly specify the cloud environment (AWS, Azure, GCP) and security concerns (data exfiltration, unauthorized access, etc.) in the prompt. 2. **Prepare Documentation**: Ensure all relevant security policies and procedures are accessible and well-organized for cross-referencing. 3. **Run the Audit**: Input the prompt into AI-Infra-Guard and let it analyze the infrastructure. Monitor the process for any interruptions or errors. 4. **Review Findings**: Carefully examine the generated report, paying attention to risk levels and recommended actions. Use the cross-referenced policy discrepancies to update documentation. 5. **Implement Mitigations**: Work with your security team to address the identified vulnerabilities, prioritizing critical risks first.
Conduct security assessments for AI systems to identify potential vulnerabilities.
Simulate various attack vectors to test the defenses of AI applications.
Identify and prioritize vulnerabilities in AI infrastructure for remediation.
Benchmark AI models against established security standards to ensure compliance.
claude install Tencent/AI-Infra-Guardgit clone https://github.com/Tencent/AI-Infra-GuardCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Conduct a comprehensive security audit of our cloud infrastructure using AI-Infra-Guard. Focus on [SPECIFIC_CLOUD_PROVIDER] environments and prioritize [SECURITY_CONCERNS]. Generate a detailed report including vulnerabilities, risk levels, and recommended mitigation steps. Cross-reference findings with our current security policies stored in [DOCUMENTATION_LOCATION].
After running AI-Infra-Guard on our AWS infrastructure with a focus on data exfiltration risks, the platform identified several critical vulnerabilities: 1. **S3 Bucket Permissions**: Found 12 publicly accessible S3 buckets containing sensitive customer data. Risk level: Critical. Recommendation: Implement least-privilege access controls and enable bucket policies with explicit deny rules. 2. **EC2 Instance Exposure**: Detected 8 EC2 instances with open security groups allowing inbound traffic from unauthorized IP ranges. Risk level: High. Recommendation: Restrict inbound traffic to known IP addresses and implement network ACLs. 3. **IAM Misconfigurations**: Identified 5 IAM roles with excessive permissions that could enable lateral movement. Risk level: High. Recommendation: Apply principle of least privilege and implement IAM access analyzer. The audit also cross-referenced these findings with our current security policies and noted discrepancies in our S3 bucket management procedures. AI-Infra-Guard suggested updating our documentation to reflect these best practices and implementing automated compliance checks.
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