T09 · Insecure Skill Coding Practices
- Location
scripts/segment.py:297- Finding
Spreadsheet Formula Injection in Exported Segmentation Results
- Content
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Vulnerability Details
File Location:
scripts/segment.py, lines 297-298
Vulnerability Type: CSV/spreadsheet formula injection
Risk Level: Medium
Classification: T09: Insecure Skill Coding PracticesVulnerable Code
python out_csv = os.path.join(output_dir, 'segmentation_results.csv') df.to_csv(out_csv, index=False, encoding='utf-8-sig')Technical Analysis
The script reads an attacker-supplied CSV into a pandas DataFrame and later exports the complete DataFrame without sanitizing text cells. This includes unrecognized columns and other attacker-controlled string values retained from the source file.
Spreadsheet applications may interpret cells beginning with formula-significant characters such as
=,+,-, or@as formulas rather than plain text. The UTF-8 BOM encoding does not prevent this behavior.An attacker could therefore place a spreadsheet formula in an input field that is preserved in
segmentation_results.csv. When an analyst opens the generated file in compatible spreadsheet software, the application may evaluate the formula. Actual behavior depends on the spreadsheet product, its version, security configuration, and user interaction.Attack Path
- An attacker creates or modifies a customer CSV record so that a retained text field begins with a formula-significant character, for example a formula that references an external resource.
- The analyst processes the file with
scripts/segment.py. load_and_clean()preserves the attacker-controlled field in the DataFrame.- The script exports the complete DataFrame to
segmentation_results.csvwithout neutralizing formula-leading values. - The analyst opens the generated CSV in spreadsheet software.
- If formula evaluation or external content is permitted, the spreadsheet application evaluates the payload in the analyst's user context.
Impact Assessment
Successful exploitation may cause the sp ...[truncated 571 chars]
- Remediation
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Remediation Suggestions
Sanitize every user-controlled string cell before CSV export. At minimum, detect values whose first non-whitespace character is
=,+,-, or@and prefix them with an apostrophe or another application-appropriate text marker.Apply the protection to all exported text columns rather than only recognized customer fields, because unknown input columns are currently preserved.
Example hardening approach:
python def neutralize_spreadsheet_formula(value): if not isinstance(value, str): return value stripped = value.lstrip() if stripped.startswith(('=', '+', '-', '@')): return "'" + value return value safe_df = df.copy() text_columns = safe_df.select_dtypes(include=['object', 'string']).columns for column in text_columns: safe_df[column] = safe_df[column].map(neutralize_spreadsheet_formula) out_csv = os.path.join(output_dir, 'segmentation_results.csv') safe_df.to_csv(out_csv, index=False, encoding='utf-8-sig')Also consider exporting to a format that supports explicit text cell types, validating the accepted input schema, discarding unnecessary columns, and documenting that generated CSV files should be opened with external links and automatic formula execution disabled.