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Mitigation Strategies Analysis for abi/screenshot-to-code

Mitigation Strategy: Strict Screenshot Source Control

  • Description:

    1. Define Approved Sources: Create a formal, documented list of the only permissible sources for screenshots (e.g., a specific, secured internal staging environment; a dedicated screenshot-taking tool running on an isolated, hardened workstation). No user uploads or external sources.
    2. Implement Access Controls: Strictly limit access to these approved sources to authorized personnel only. Use role-based access control (RBAC) within the staging environment or on the dedicated workstation.
    3. Automated Screenshot Generation (Ideal): Automate the screenshot capture process using a script or tool that runs within the controlled environment. This eliminates manual handling and reduces human error/malice. The script should be reviewed for security.
    4. Audit Logging: Implement comprehensive audit logging to track every screenshot taken: who took it, when, from what source (down to the specific URL or application state), and any metadata associated with the capture.
    5. Regular Review: Periodically (e.g., monthly, quarterly) review the approved sources list, access controls, and audit logs to ensure they remain appropriate and effective.

  • Threats Mitigated:

    • Exposure of Sensitive Information (High Severity): Prevents unauthorized screenshots containing API keys, passwords, PII, or other confidential data from ever being fed into screenshot-to-code.
    • Intellectual Property Theft/Leakage (High Severity): Reduces the risk of proprietary UI designs or confidential application states being captured and used for malicious purposes (e.g., cloning).
    • Prompt Injection/Manipulation (Medium Severity): Makes it significantly harder for attackers to introduce crafted malicious screenshots, as they cannot easily inject them into the controlled pipeline.

  • Impact:

    • Exposure of Sensitive Information: Risk significantly reduced (e.g., 80-90% reduction).
    • Intellectual Property Theft/Leakage: Risk significantly reduced (e.g., 70-80% reduction).
    • Prompt Injection/Manipulation: Risk moderately reduced (e.g., 40-50% reduction).

  • Currently Implemented:

    • Partially implemented. Screenshots are currently taken from a staging environment, but access controls are not strictly enforced. Audit logging is basic.

  • Missing Implementation:

    • Formal documentation of approved sources is missing.
    • RBAC is not fully implemented within the staging environment.
    • Automated screenshot generation is not yet implemented.
    • Audit logging needs to be more comprehensive (including specific URLs/application states).

Mitigation Strategy: Screenshot Pre-processing and Filtering

  • Description:

    1. Identify Sensitive Areas: Create a comprehensive list of UI elements, text patterns, or screen regions that are likely to contain sensitive data (e.g., password fields, API key displays, user profile details, internal URLs).
    2. Choose Redaction Method: Select a redaction method that balances security and usability. Blurring is generally preferred over blacking out, as it preserves the overall layout for the AI while still obscuring the sensitive data.
    3. Implement Automated Redaction: This is the core step. Choose one of the following approaches (or a combination):
      • Option A (Image Processing with OpenCV): Use OpenCV (or a similar library) to detect specific UI elements (text fields, buttons, etc.) based on their visual characteristics. Train a model (or use a pre-trained model) to identify these elements and apply the blurring/redaction.
      • Option B (OCR + Regex): Use Optical Character Recognition (OCR) to extract all text from the screenshot. Then, use regular expressions to identify and redact sensitive data based on patterns (e.g., [A-Za-z0-9]{32} for a potential API key, email address patterns, etc.).
      • Option C (Bounding Box Restrictions): Define precise rectangular regions within the screenshot that are allowed to be processed. Exclude all other areas. This is the most restrictive but also the most secure if sensitive data is consistently located in predictable areas.
    4. Manual Review (High-Security Cases): For applications handling highly sensitive data, add a mandatory manual review step. A human verifies the automated redaction before the screenshot is passed to screenshot-to-code.
    5. Testing and Refinement: Thoroughly test the redaction process with a wide variety of screenshots to ensure it's effective and doesn't accidentally redact non-sensitive information or leave sensitive data exposed. Iteratively refine the process.
    6. Metadata Stripping: Remove any metadata from the screenshot before processing.

  • Threats Mitigated:

    • Exposure of Sensitive Information (High Severity): Directly prevents sensitive data within the screenshot from being exposed to the AI model, thus preventing it from being incorporated into the generated code.
    • Prompt Injection/Manipulation (Medium Severity): Reduces the opportunity for attackers to use visually encoded data (e.g., cleverly disguised text) within the screenshot to subtly influence the AI's output.

  • Impact:

    • Exposure of Sensitive Information: Risk significantly reduced (e.g., 90-95% reduction, highly dependent on the thoroughness of the redaction).
    • Prompt Injection/Manipulation: Risk moderately reduced (e.g., 30-40% reduction).

  • Currently Implemented:

    • Not implemented.

  • Missing Implementation:

    • The entire pre-processing and filtering pipeline needs to be implemented. This includes selecting a redaction method, implementing the automated redaction (using one of the options described), potentially adding a manual review step, and rigorous testing.

Mitigation Strategy: "Screenshot Prompt" Engineering and Output Filtering

  • Description:

    1. Controlled Screenshot Composition: Be extremely deliberate about what is included in the screenshot. Avoid unnecessary UI elements, text, or visual clutter that could confuse the AI or be misinterpreted.
    2. Visual Cues (Optional): Consider adding subtle visual cues or markers to the screenshot to guide the AI's interpretation. For example, you could use specific colors or borders to highlight important areas or indicate the intended structure of the UI. (This is experimental and requires careful testing).
    3. Output Filtering (Post-Generation): Implement filters that analyze the output (generated code) from screenshot-to-code to detect and block potentially malicious code patterns before it's used.
      • Regular Expressions: Use regular expressions to identify and remove suspicious code snippets, such as attempts to execute shell commands (system(), exec()), access sensitive files, or make network connections to unexpected domains.
      • Keyword Blacklists: Maintain a list of prohibited keywords or function calls that should never appear in the generated code (e.g., eval(), subprocess.Popen(), specific database connection strings).
      • AST Analysis (Advanced): Use Abstract Syntax Tree (AST) analysis to parse the generated code and identify potentially dangerous constructs or patterns at a deeper level than regex.
    4. Limit Functionality: Limit the functionality of the generated code. For example, if the code is only supposed to generate HTML, ensure that it cannot generate code that interacts with the backend.

  • Threats Mitigated:

    • Prompt Injection/Manipulation (Medium Severity): Directly addresses the risk of attackers crafting screenshots to trick the AI into generating malicious code. Controlled composition makes this harder; output filtering catches attempts that slip through.
    • Inaccurate/Malicious Code Generation (Medium Severity): Output filtering can catch some instances of incorrect or malicious code that result from misinterpretations by the AI.
    • Hallucinations (Medium Severity): Output filtering can catch some instances of code that is not present in the screenshot.

  • Impact:

    • Prompt Injection/Manipulation: Risk moderately reduced (e.g., 50-60% reduction, combining both input and output controls).
    • Inaccurate/Malicious Code Generation: Risk slightly reduced (e.g., 20-30% reduction, as filtering is not foolproof).
    • Hallucinations: Risk slightly reduced (e.g., 20-30% reduction).

  • Currently Implemented:

    • Not implemented.

  • Missing Implementation:

    • Develop guidelines for controlled screenshot composition.
    • Implement output filtering using regular expressions, keyword blacklists, and potentially AST analysis.
    • Thoroughly test the filtering rules to ensure they don't block legitimate code.