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Attack Surface Analysis for Fabric Agent Action

Attack Surface Identification

  • Digital Assets, Components, and System Entry Points:

    • GitHub Action (fabric-agent-action):

      • Automates workflows in GitHub repositories.
      • Implementation Details: Defined in action.yml, source code in fabric_agent_action/ directory.

    • LLM API Integrations:

      • Interfaces with external Large Language Models (LLMs) via APIs.
      • Supports providers:
        • OpenAI
        • OpenRouter
        • Anthropic
      • Implementation Details: Configuration in app.py, API interactions in llms.py.

    • Environment Variables and Secrets:

      • Stores sensitive API keys:
        • OPENAI_API_KEY
        • OPENROUTER_API_KEY
        • ANTHROPIC_API_KEY
      • Implementation Details: Secrets are set in GitHub workflows and accessed in the action code.

    • User Inputs:

      • Issue Comments and Pull Request Comments:

        • Users trigger the action using commands like /fabric.
        • Implementation Details: Handled in workflows like fabric-issue-agent-react.yml, processed in app.py.

      • Input Files:

        • The action processes input files specified in workflows.
        • Implementation Details: Input handling in app.py, agents.py.

    • Outputs:

      • GitHub Comments:

        • The action posts results back as comments on issues or pull requests.
        • Implementation Details: Uses peter-evans/create-or-update-comment@v4 in workflows.

      • Output Files:

        • Generates output files from processed inputs.
        • Implementation Details: Output handling in app.py.

    • Docker Environment:

      • Runs within a Docker container.
      • Implementation Details: Docker image defined in Dockerfile, action entry point in entrypoint.sh.

    • Scripts and Automation Tools:

      • Downloading Patterns:
        • scripts/download_fabric_patterns.sh retrieves patterns from external repositories.
      • Generating Code:
        • scripts/generate_fabric_tools.py generates code using LLMs.
      • Implementation Details: Located in scripts/ directory.

    • Fabric Patterns:

      • Uses patterns from external sources to guide LLM behavior.
      • Implementation Details: Stored in prompts/fabric_patterns/.

    • GitHub Workflows:

      • Automations defined in workflows:
        • ci.yaml for Continuous Integration.
        • publish.yaml for publishing docker images.
        • update-fabric-patterns.yaml for updating patterns.
      • Implementation Details: Located in .github/workflows/ directory.

  • Potential Vulnerabilities or Insecure Configurations:

    • Secrets Management:

      • Risk of API keys being exposed if not securely managed.
      • Potential logging of sensitive information.

    • User Input Handling:

      • Vulnerable to prompt injection or code injection attacks via unvalidated user inputs.
      • Malicious commands in issue or PR comments could exploit the action.

    • External Dependencies:

      • Downloads and executes code from external repositories without integrity checks.
      • Patterns and scripts could be tampered with.

    • Lack of Access Control:

      • Unauthorized users may trigger the action in public repositories.
      • Insufficient validation of user permissions.

    • Rate Limiting and Resource Usage:

      • No mechanisms to prevent abuse through excessive triggering of the action.
      • Risk of Denial of Service (DoS) due to resource exhaustion.

    • Logging Practices:

      • Insufficient logging for auditing and tracking actions.
      • Potential for sensitive data to be logged inadvertently.

  • Reference Implementation Details:

    • Action Configuration: action.yml

    • Docker Image Definition: Dockerfile

    • Scripts:

      • Pattern download script: scripts/download_fabric_patterns.sh
      • Fabric tools generation: scripts/generate_fabric_tools.py

    • Source Code: fabric_agent_action/ directory containing:

      • agents.py
      • app.py
      • config.py
      • fabric_tools.py
      • llms.py

    • GitHub Workflows:

      • Continuous Integration: .github/workflows/ci.yaml
      • Publishing Images: .github/workflows/publish.yaml
      • Updating Patterns: .github/workflows/update-fabric-patterns.yaml

Threat Enumeration

1. Information Disclosure (API Keys Leakage)

  • Description: API keys (OPENAI_API_KEY, OPENROUTER_API_KEY, ANTHROPIC_API_KEY) could be exposed through logs, outputs, or improper handling within the action.

  • Attack Vectors:

    • Malicious inputs causing the action to output secrets.
    • Accidental logging of secrets in action outputs or logs.

  • Conditions Required:

    • Inadequate handling and protection of environment variables.
    • Insufficient sanitization of outputs and logs.

  • Affected Components:

    • Environment variables storing API keys.
    • Action code in app.py, llms.py handling secrets.

2. Elevation of Privilege (Prompt Injection Leading to Code Execution)

  • Description: Malicious user inputs manipulate LLM prompts to execute unauthorized code or commands within the action environment.

  • Attack Vectors:

    • Users submit crafted inputs in issue/PR comments that alter LLM behavior.
    • Exploitation of LLM responses to inject code or commands.

  • Conditions Required:

    • Lack of robust input validation and sanitization.
    • LLMs processing untrusted inputs without limitations.

  • Affected Components:

    • User input processing in app.py, agents.py.
    • LLM interactions in llms.py, fabric_tools.py.

3. Tampering (Modification of Patterns or Code)

  • Description: Attackers modify downloaded patterns or scripts to inject malicious code into the action.

  • Attack Vectors:

    • Compromise of the external repository hosting Fabric Patterns.
    • Man-in-the-middle attacks altering patterns during download.

  • Conditions Required:

    • Absence of integrity checks (e.g., checksums) for downloaded content.
    • Automatic execution of unverified external code.

  • Affected Components:

    • scripts/download_fabric_patterns.sh
    • Pattern files in prompts/fabric_patterns/

4. Denial of Service (Resource Exhaustion via Excessive Triggering)

  • Description: Excessive triggering of the action leads to resource exhaustion, impacting availability and incurring costs.

  • Attack Vectors:

    • Flooding the repository with comments containing /fabric.
    • Automated scripts triggering the action repeatedly.

  • Conditions Required:

    • Lack of rate limiting or quotas.
    • Open access for triggering the action in public repositories.

  • Affected Components:

    • GitHub workflows responding to issue_comment, pull_request events.
    • LLM API usage in llms.py.

5. Spoofing (Unauthorized Usage of the Action)

  • Description: Unauthorized users trigger the action, potentially leading to unintended operations and resource consumption.

  • Attack Vectors:

    • External users posting comments to trigger the action.
    • Lack of authentication checks in workflows.

  • Conditions Required:

    • Insufficient verification of user identity in workflows.
    • Public repositories without access controls.

  • Affected Components:

    • GitHub workflows in .github/workflows/
    • Access control logic in workflows and action code.

6. Repudiation (Insufficient Logging and Auditing)

  • Description: Insufficient logging makes it difficult to trace actions and hold users accountable.

  • Attack Vectors:

    • Actions executed without sufficient audit trails.
    • Disputes over actions performed due to lack of evidence.

  • Conditions Required:

    • Inadequate logging mechanisms in the action.
    • Logs not securely stored or easily tampered with.

  • Affected Components:

    • Logging configurations in app.py, config.py.
    • Logging output destinations.

Impact Assessment

1. Information Disclosure (API Keys Leakage)

  • Potential Impact on CIA Triad:

    • Confidentiality: Compromised—Exposed API keys can be misused.
    • Integrity: Compromised—Attackers can manipulate LLM interactions.
    • Availability: Compromised—Misuse of APIs can exhaust quotas.

  • Severity Assessment:

    • Damage: High—Unauthorized access to services, potential data breaches.
    • Likelihood: Medium—Possible through misconfiguration or exploitation.
    • Existing Controls: Use of GitHub Secrets, but may lack additional safeguards.
    • Data Sensitivity: Confidential—API keys are sensitive.
    • User Impact: All users—Affects entire system functionality.
    • System Impact: Full system
    • Business Impact: Critical—Financial loss, reputational damage, legal consequences.

  • Prioritization: Critical Impact

2. Elevation of Privilege (Prompt Injection Leading to Code Execution)

  • Potential Impact on CIA Triad:

    • Integrity: Compromised—Execution of unauthorized code.
    • Availability: Compromised—Service disruption, potential data corruption.

  • Severity Assessment:

    • Damage: High—Possible system compromise.
    • Likelihood: High—If inputs are not properly sanitized.
    • Existing Controls: May lack comprehensive input validation.
    • Data Sensitivity: Internal/Confidential
    • User Impact: All users
    • System Impact: Full system
    • Business Impact: Critical—Severe security breach potential.

  • Prioritization: Critical Impact

3. Tampering (Modification of Patterns or Code)

  • Potential Impact on CIA Triad:

    • Integrity: Compromised—Execution of malicious code.
    • Confidentiality: Potential exposure of sensitive data.

  • Severity Assessment:

    • Damage: High—Risk of widespread compromise.
    • Likelihood: Medium—Requires external compromise.
    • Existing Controls: Lack of integrity verification.
    • Data Sensitivity: Internal
    • User Impact: All users
    • System Impact: Full system
    • Business Impact: High—Significant operational impact.

  • Prioritization: High Impact

4. Denial of Service (Resource Exhaustion via Excessive Triggering)

  • Potential Impact on CIA Triad:

    • Availability: Compromised—Service outage, unavailability.

  • Severity Assessment:

    • Damage: Medium to High—Service disruptions.
    • Likelihood: High—Easy to exploit without controls.
    • Existing Controls: Possibly none.
    • Data Sensitivity: N/A
    • User Impact: All users
    • System Impact: Component/System
    • Business Impact: High—Financial costs, degraded user experience.

  • Prioritization: High Impact

5. Spoofing (Unauthorized Usage of the Action)

  • Potential Impact on CIA Triad:

    • Availability: Potentially compromised—Unintended resource usage.
    • Integrity: Possibly affected if unauthorized actions alter system state.

  • Severity Assessment:

    • Damage: Medium—Resource misuse.
    • Likelihood: High—Common in public repositories.
    • Existing Controls: May rely on GitHub defaults.
    • Data Sensitivity: Public
    • User Impact: All users
    • System Impact: Component
    • Business Impact: Medium—Increased costs, potential misuse.

  • Prioritization: Medium Impact

6. Repudiation (Insufficient Logging and Auditing)

  • Potential Impact on CIA Triad:

    • Integrity: Compromised—Inability to verify actions.
    • Accountability: Affected—Users can deny actions.

  • Severity Assessment:

    • Damage: Low—Primarily impacts traceability.
    • Likelihood: Medium—If logging is not prioritized.
    • Existing Controls: May be insufficient.
    • Data Sensitivity: N/A
    • User Impact: All users
    • System Impact: Component
    • Business Impact: Low—Potential compliance issues.

  • Prioritization: Low Impact

Threat Ranking

  1. Information Disclosure (API Keys Leakage) — Critical

    • Justification: Exposure of API keys can lead to severe security breaches, unauthorized access, and significant financial losses.

  2. Elevation of Privilege (Prompt Injection Leading to Code Execution) — Critical

    • Justification: Allows attackers to execute arbitrary code, potentially compromising the entire system.

  3. Tampering (Modification of Patterns or Code) — High

    • Justification: Malicious code execution can result from tampered patterns or scripts, affecting system integrity.

  4. Denial of Service (Resource Exhaustion via Excessive Triggering) — High

    • Justification: Resource exhaustion impacts availability and can cause service outages.

  5. Spoofing (Unauthorized Usage of the Action) — Medium

    • Justification: Unauthorized action usage leads to resource misuse and potential unintended consequences.

  6. Repudiation (Insufficient Logging and Auditing) — Low

    • Justification: Affects the ability to audit and trace actions but poses less immediate risk.

Mitigation Recommendations

1. Information Disclosure (API Keys Leakage)

  • Recommendations:

    • Secure Secret Management:
      • Ensure API keys are stored using GitHub Secrets.
      • Avoid hardcoding secrets in code or configurations.
    • Code Review and Static Analysis:
      • Regularly audit code to ensure secrets are not exposed.
      • Use tools like GitHub's secret scanning.
    • Limit Scope of Secrets:
      • Use API keys with minimal required permissions.
      • Rotate API keys regularly.
    • Sanitize Outputs and Logs:
      • Ensure that logs do not contain sensitive information.
      • Implement strict controls on what information is outputted.

  • References:

2. Elevation of Privilege (Prompt Injection Leading to Code Execution)

  • Recommendations:

    • Input Validation and Sanitization:
      • Implement robust validation for all user inputs.
      • Reject or sanitize inputs containing potentially harmful content.
    • LLM Prompt Hardening:
      • Design prompts to be resilient against injection.
      • Limit LLM capabilities to prevent execution of unintended commands.
    • Use Safe LLM Practices:
      • Employ stop sequences or output filtering.
      • Monitor and log LLM outputs for suspicious content.

  • References:

3. Tampering (Modification of Patterns or Code)

  • Recommendations:

    • Integrity Verification:
      • Use checksums or cryptographic signatures to verify downloads.
      • Utilize secure channels (HTTPS) with certificate validation.
    • Dependency Management:
      • Pin dependencies to specific versions or commits.
      • Regularly update and audit external dependencies.
    • Code Review:
      • Review downloaded scripts and patterns before use.
      • Implement approval processes for updates.

  • References:

4. Denial of Service (Resource Exhaustion via Excessive Triggering)

  • Recommendations:

    • Access Control in Workflows:
      • Modify workflows to restrict action triggering to authorized users.
      • Use conditional checks like if: github.actor == 'authorized-user'.
    • Rate Limiting:
      • Implement limits on how often the action can be triggered.
      • Use flags or tokens to prevent repeated triggers.
    • Monitoring and Alerts:
      • Monitor action usage and set up alerts for unusual activity.
      • Track API usage quotas and costs.

  • References:

5. Spoofing (Unauthorized Usage of the Action)

  • Recommendations:

    • User Authentication Checks:
      • Verify the identity of users triggering the action.
      • Implement conditions to check user roles or membership.
    • Restrict Public Access:
      • Limit action triggers to internal collaborators.
      • Use private repositories when possible.
    • Token Scope Limitation:
      • Limit the permissions of the GITHUB_TOKEN used in actions.

  • References:

6. Repudiation (Insufficient Logging and Auditing)

  • Recommendations:

    • Enhanced Logging:
      • Log all action invocations with relevant details.
      • Ensure logs include timestamps, user identities, and actions performed.
    • Secure Log Storage:
      • Protect logs from unauthorized access or tampering.
      • Use append-only storage mechanisms.
    • Compliance with Standards:
      • Align logging practices with regulatory requirements.

  • References:

QUESTIONS & ASSUMPTIONS

  • Questions:

    1. Input Validation:
      • Are there existing measures to validate and sanitize user inputs, particularly in app.py and agents.py?
      • How does the action prevent prompt injection attacks?
    2. Secrets Management:
      • Are secrets ever logged or outputted, intentionally or unintentionally?
      • What measures are in place to rotate and revoke API keys if compromised?
    3. External Dependencies:
      • Is there any integrity verification for downloaded Fabric Patterns and scripts?
      • How often are dependencies reviewed and updated?
    4. Access Control:
      • Do the workflows restrict action execution to authorized users?
      • How are users authenticated and authorized within the action?
    5. Rate Limiting and Monitoring:
      • Are there mechanisms to detect and prevent excessive triggering of the action?
      • What monitoring is in place for API usage and potential abuse?

  • Assumptions:

    • Input Handling: It is assumed that user inputs are not currently subjected to thorough validation and sanitization.
    • Logging Practices: Assuming that logging is minimal and may not include sufficient detail for auditing.
    • Secrets Management: Assuming that API keys are properly stored using GitHub Secrets but may not have additional protection against inadvertent exposure.
    • External Dependencies: It is assumed that there is no integrity verification for downloaded patterns and scripts.