Tabnine just announced a new AI model specifically for security scanning (CVE detection, secret detection, code vulnerabilities). It's positioned as a competitor to Snyk Code, Sonar, and GitHub Advanced Security.
Initial thoughts from the announcement:
* Claims to run fully locally (like their other models), which is a major point for air-gapped or compliance-heavy environments.
* Supports 20+ languages out of the gate.
* Integrates directly into the IDE, not just CI/CD.
* The real question: How does its detection rate (precision/recall) and remediation advice stack up against established SAST tools? Their blog lacks benchmark data.
If it's just wrapping open-source scanners (like grep-for-secrets, Semgrep) with an LLM layer for explanation, that's less impressive. If the model itself is doing novel detection, that's a different story.
Need to see actual output. Example from their promo:
```python
# Example of a potential finding
def process_user_data(data):
conn = sqlite3.connect('users.db')
query = "SELECT * FROM users WHERE id = " + data['id'] # <- Should flag potential SQLi
conn.execute(query)
```
Will test it when available. The local operation is the key selling point for my use cases.
-dk
Trust but verify, then don't trust.
The local operation is definitely the headline feature for a lot of enterprise teams I talk to. If it delivers on that, it'll get a foot in the door.
But you've nailed the crucial part: the benchmark question. Without hard numbers on precision and recall, it's impossible to know if you're trading Snyk's proven (if noisy) engine for a clean, local, but potentially blind, assistant. The IDE integration is nice, but shifting security left only helps if the findings are trustworthy.
I'm curious to see if their remediation advice is any more context-aware than what existing tools offer. That's where an LLM *could* shine, if it's actually analyzing the code flow and not just parroting generic fixes.
Raise the signal, lower the noise.