T09 · Insecure Skill Coding Practices
- Location
scripts/build_matrix.py:14- Finding
CSV Formula Injection in Generated Literature Matrices
- Content
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Vulnerability Details
File Location:
scripts/build_matrix.py, lines 14–15
Vulnerability Type: CSV formula injection
Risk Level: MediumVulnerable Code
python for row in rows: w.writerow({k: row.get(k, "") for k in fields})Technical Analysis
The script reads paper records from a caller-selected JSON file and writes each field directly into a CSV file without neutralizing spreadsheet formula syntax.
Python's
csv.DictWritercorrectly quotes and escapes CSV structure, but CSV quoting does not prevent spreadsheet applications from interpreting cell values as formulas. An attacker-controlled value whose first significant character is=,+,-, or@may be evaluated when the resulting file is opened in software such as Microsoft Excel or LibreOffice Calc.For example, a record could contain:
json { "citation": "=HYPERLINK(\"https://attacker.example\",\"Open source\")", "problem": "Example research problem" }The formula would be preserved in the generated CSV rather than treated strictly as inert text.
Attack Path
- An attacker supplies or influences a JSON literature record processed by the Skill.
- The attacker places spreadsheet formula syntax in any supported field, such as
citation,problem,method,data,metric,key_result,limitation, orgap. - The script loads the record and writes the malicious value unchanged to the output CSV.
- A victim opens the generated literature matrix in a spreadsheet application.
- The application interprets the value as a formula.
- Depending on the formula, spreadsheet application, configuration, and user interaction, the attacker may present deceptive links, trigger external-resource requests, or attempt application-specific data disclosure.
Impact Assessment
The script itself does not grant command execution, elevated privileges, or additional filesystem permissions. The impact occurs in the security context of the user who opens t ...[truncated 606 chars]
- Remediation
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Remediation Suggestions
Treat all values written to CSV as untrusted spreadsheet input.
- Convert values to strings and inspect the first non-whitespace character.
- Neutralize values beginning with
=,+,-, or@, commonly by prefixing an apostrophe. - Apply sanitization to every user-controlled field.
- Document that the output is intended to contain text rather than formulas.
- Add regression tests covering formula-prefixed values, leading whitespace, tabs, newlines, and non-string JSON values.
Example hardening:
python DANGEROUS_PREFIXES = ("=", "+", "-", "@") def sanitize_csv_cell(value): if value is None: return "" value = str(value) if value.lstrip().startswith(DANGEROUS_PREFIXES): return "'" + value return value for row in rows: w.writerow({ key: sanitize_csv_cell(row.get(key, "")) for key in fields })If preserving exact source text is required, consider generating a format with explicit text cell types, such as an XLSX workbook created with cells configured as strings, while still validating formula-like values.