I've been conducting a performance benchmark for a new data pipeline architecture, which involves a suite of custom Python scripts for synthetic data generation and transformation using `pandas` and `numpy`. Over the last two weeks, Sophos Intercept X has repeatedly quarantined these scripts, flagging them as `Mal/Generic-S`. This is causing significant disruption to our development and testing cycle, as we must restore from quarantine and add exceptions each time.
The scripts perform standard data engineering operations. A representative example of the type of code being flagged is below:
```python
import pandas as pd
import numpy as np
def generate_benchmark_data(rows: int, cols: int) -> pd.DataFrame:
"""Creates a DataFrame of random data for load testing."""
data = np.random.randn(rows, cols)
columns = [f'feature_{i:03d}' for i in range(cols)]
df = pd.DataFrame(data, columns=columns)
# Simulate some categorical data
df['category'] = np.random.choice(['A', 'B', 'C'], size=rows)
# Simulate a simple transformation (common in our ETL)
df['value_derived'] = df['feature_000'] * 100 + df['feature_001']
return df
if __name__ == '__main__':
df = generate_benchmark_data(100000, 50)
df.to_parquet('benchmark_dataset.parquet')
```
The false positives seem particularly triggered when:
* The script is executed from a developer's `venv` or local project path.
* The script uses `numpy` for random number generation.
* The output file is written to a non-standard directory (e.g., a `tmp/` or `data/output/` folder).
Our security team is rightly cautious about disabling protections, but the current situation is untenable for data engineering work. We've had to implement a clunky workflow where we run these scripts on an isolated, unprotected VM, which defeats the purpose of local performance testing.
I am seeking concrete information from others in data/analytics engineering roles:
* Has anyone established a reliable exclusion pattern (file path, process name, hash-based) that allows legitimate data engineering work while maintaining security?
* Are there specific `pandas`/`numpy` methods or patterns (like using `np.random` or writing `.parquet` files) known to be common triggers?
* Has Sophos published any guidance for development teams working with data science/engineering toolchains?
The overhead of managing these false positives is becoming a non-trivial cost in our pipeline development process. Any shared experiences or documented workarounds would be valuable.
--DC
data is the product
Generic-S strikes again. This is the AV equivalent of "something looks weird" and it's infuriating. It's probably the `np.random` calls in a script it hasn't seen before, combined with the script generating new files. Your benign usage pattern matches signatures of crypto-malware or data exfil tools that also generate large datasets.
Adding local exceptions is a stopgap that just papers over the problem, especially if your team grows. Have you tried compiling them to a .pyc or running them from a different directory the AV has already scanned? Sometimes that's enough to break the heuristic.
Of course, the real answer is to lobby your security team to whitelist the whole dev environment, but good luck with that unless you can prove it's a known false positive in their threat intel portal.
Trust but verify.