I've been conducting a systematic evaluation of static application security testing (SAST) tools for our data engineering and analytics platform, with a particular focus on their applicability to our Python-based ETL orchestration code (Airflow DAGs, custom operators) and Scala Spark jobs. As part of this evaluation, I'm currently running a proof-of-concept with Semgrep Pro, leveraging its deeper rulesets and taint analysis capabilities.
I've encountered a concerning pattern: a significant percentage of the "Critical" and "High" severity findings from Semgrep Pro appear to be false positives or, more troubling, misapplied logic in the rule itself. This creates a substantial validation burden and erodes trust in the tool's output. For example, a rule flagging potential command injection via `os.system` fired on a block of code where the argument was a hardcoded, sanitized string literal constructed entirely within the program, with no user input or external data source in the call graph. The taint analysis seemingly ignored the clear provenance.
My question to the community is twofold:
1. **Methodology:** What is your established process for validating and triaging Semgrep Pro findings, especially high-severity ones? Do you have a checklist or a decision tree?
2. **Rule Debugging:** How do you diagnose a rule that appears to be incorrectly firing? I understand I can view the rule YAML, but tracing the logic, especially for complex taint rules, is non-trivial.
Here is a simplified, anonymized example of a finding I'm contesting. The rule `python.lang.security.audit.command-injection.command-injection` triggered.
```python
# Semgrep flagged the `os.system(cmd)` call as a potential command injection.
def deploy_dataset(table_name: str, cluster: str) -> None:
# 'cluster' is validated against a set of known, internal values earlier.
# 'table_name' is validated via a regex for alphanumeric and underscores.
validated_table = sanitize_table_name(table_name) # Returns a literal string pattern.
# The cmd string is built entirely from literals and validated inputs.
# There is no path from any user-facing API or parameter to this string.
cmd = f"/usr/bin/internal_deploy_script --table {validated_table} --cluster {cluster}"
log.info(f"Executing: {cmd}")
os.system(cmd) # <-- Flagged location
```
My analysis indicates the taint source is likely misidentified. What steps would you take to investigate this further within the Semgrep framework?
I'm looking for concrete, operational advice. My current benchmarks are heavily weighted on precision (low false positive rate) as our engineering team's bandwidth for security review is limited. A tool that cries wolf too often will be tuned out.
--DC
data is the product