My 2025 Python Code Audit: 7 Days with Distrust Revealed

Every developer knows the feeling. You look at a codebase you’ve worked on for years, and a quiet sense of unease creeps in. It’s the ghost of technical debt, the shadow of deprecated packages, and the whisper of vulnerabilities you hope aren’t there. As 2025 dawned, I decided to confront these ghosts head-on. My weapon of choice? A new, slightly intimidating tool called Distrust. I gave myself seven days to let it tear my code apart. The results were more revealing than I ever imagined.

This wasn't just another pass with Flake8 or Pylint. This was a deep, semantic, and security-focused audit that fundamentally changed how I view code quality. If you're a Python developer who cares about writing robust, secure, and maintainable code, this week-long journey is for you.

What is Distrust and Why Did I Choose It?

In a world saturated with static analysis tools, what makes Distrust different? Traditional linters are excellent at enforcing style guides (PEP 8) and catching simple programmatic errors like unused variables. They are the grammar checkers of code. Distrust, however, aims to be a literary critic and a security expert rolled into one.

It’s an AI-powered static analysis engine that goes beyond syntax and style. Its core propositions are:

• Semantic Understanding: It doesn't just see a function call; it attempts to understand the flow of data through your application, flagging potential data leaks or insecure transformations that a normal linter would miss.
• Security-First Analysis: It’s pre-loaded with an extensive database of Common Vulnerabilities and Exposures (CVEs) and common weakness enumerations (CWEs). It scans not just your code but your entire dependency tree for known and potential exploits.
• Maintainability Metrics: It uses machine learning models trained on millions of open-source projects to identify “code smells” that lead to future bugs and maintenance nightmares, such as overly complex logic or brittle architecture patterns.

I chose it because my project, a mid-sized data processing API, had reached a level of complexity where simple linting felt like putting a bandage on a broken leg. I needed a tool that would force me to be honest about the code's real health.

The 7-Day Audit Plan: A Day-by-Day Breakdown

Days 1-2: Setup and The Initial Shock

Getting Distrust running was straightforward—a simple `pip install distrust` and a `distrust init` command to create a configuration file. I pointed it at my project's root directory and ran `distrust scan`. I expected a few dozen issues. The report came back with 417 findings.

My heart sank. But as I scrolled through, I realized most weren't PEP 8 violations. They were categorized by severity: 12 `CRITICAL`, 55 `HIGH`, 150 `MEDIUM`, and the rest `LOW`. The critical issues were terrifying: things like “Insecure Deserialization using `pickle`” in a forgotten admin script and “Transitive Dependency with Known RCE Vulnerability (CVE-2024-XXXXX)” buried deep in my `requirements.txt`.

Days 3-4: Tackling High-Impact Vulnerabilities

The next two days were a blur of focused, high-stakes coding. I started with the 12 critical issues. The `pickle` vulnerability was an easy fix—I replaced it with `json` for the simple data I was handling. The real challenge was the vulnerable transitive dependency. Distrust showed me the chain: My app -> `cool-library==1.2` -> `shady-parser==0.9`. `shady-parser` had a remote code execution flaw.

Fixing it required upgrading `cool-library` to a major new version, which introduced breaking changes. This was classic technical debt payback. Two days of refactoring, but at the end, I had eliminated all `CRITICAL` and `HIGH` security risks. The peace of mind was immediate and profound.

Days 5-6: The Deep Dive into Semantic Code Smells

With the critical fires out, I moved on to the more nuanced findings. This is where Distrust truly shone. It flagged a function for “High Cyclomatic Complexity coupled with Unsafe Data Handling.” Here’s a simplified example of the pattern it found:

# BEFORE: Distrust flagged this for complex logic and potential data leakage
def process_user_data(user, data):
    if user.is_admin:
        # ... complex logic ...
        processed_data = legacy_processor(data)
    else:
        # ... other complex logic ...
        processed_data = standard_processor(data)

    # This log could accidentally include PII from the admin branch
    log.info(f"Processed data: {processed_data}")
    return processed_data

The tool pointed out that a bug in `legacy_processor` could cause Personally Identifiable Information (PII) to be logged. It couldn't *prove* it would happen, but it saw the risk. I refactored this into smaller, more predictable functions with explicit data contracts, completely isolating the admin and standard user data flows. This wasn't a bug... yet. But it was an accident waiting to happen, and Distrust saw it.

Day 7: Reflection and The New Standard

On the final day, I re-ran the scan. The report showed 0 `CRITICAL`, 0 `HIGH`, and only 28 `MEDIUM` issues left to address at a slower pace. The codebase felt lighter, safer, and more robust. My key takeaway was that my previous definition of “clean code” was incomplete. Clean code isn't just readable; it's also auditable, secure, and resilient against its own dependencies. This 7-day trial by fire established a new, higher standard for my work going forward.

Distrust vs. Traditional Linters: A Head-to-Head Comparison

To put its value into perspective, here’s how Distrust stacks up against the tools most Python developers use daily.

My 3 Most Shocking Revelations

1. Your Dependencies Are Your Biggest Blind Spot: I meticulously vet my direct dependencies in `requirements.txt`. I never considered that a trusted library could pull in a vulnerable *transitive* dependency. Tools that scan the entire dependency tree are no longer optional in 2025; they are essential.
2. “Correct” Code Can Still Be Unsafe: The function that Distrust flagged for potential data leakage passed all our unit tests and worked perfectly in production. It was logically correct but structurally unsafe. This highlights the gap between testing for correctness and auditing for security and robustness.
3. AI Can Spot Human Patterns: One of the most fascinating flags was “Inconsistent Error Handling Logic.” The tool noticed that in some parts of the API I returned a `500` error with a JSON body, while in others, I raised an exception that resulted in a plain text `500`. It recognized this as a pattern of inconsistent design that would be confusing for API clients. That's a level of insight I'd only expect from a senior peer reviewer.

Is a 'Distrust' Audit Worth It for Your Python Project?

Absolutely, but with a caveat. A tool like Distrust is not a replacement for Pylint or Flake8; it's a powerful addition. You still need a fast linter in your CI/CD pipeline for immediate feedback on every commit.

An intensive, Distrust-style audit is something you should schedule periodically—perhaps quarterly or biannually. It’s an investment. The first run will be painful and time-consuming. But the long-term payoff is a drastically reduced risk of security breaches, fewer production incidents, and a codebase that’s easier and safer for your team to evolve.

The era of just checking for style is over. In 2025, we must embrace a deeper, more distrustful approach to our own code to build the resilient systems the future requires.