Feature request (with my own implementation): add a plugin which detects common SSRF cases where user-controlled URLs flow into outbound HTTP requests

Is your feature request related to a problem? Please describe.
Yes. Bandit currently misses a common SSRF pattern in Python services: user-controlled URLs from request data flowing into outbound HTTP clients such as requests, httpx, or urllib.

The existing HTTP-related plugins do not really cover this:

• B113 checks missing timeout
• B501 checks verify=False
• B615 is specific to Hugging Face downloads

So in practice, Bandit can miss real SSRF bugs in code that downloads files or resources from user-provided URLs.

A concrete example is CVE-2025-54381 in BentoML:

• Advisory: https://github.com/advisories/GHSA-mrmq-3q62-6cc8
• Fix: https://github.com/bentoml/BentoML/commit/534c3584621da4ab954bdc3d814cc66b95ae5fb8

Similar issues also show up in projects like LLaMA-Factory (CVE-2025-61784), LNbits (CVE-2025-32013), and D-Tale (CVE-2024-21642).

Describe the solution you'd like
I would like Bandit to have a plugin for SSRF-style flows where:

• the source is request-controlled input such as request.body(), request.args, request.form(), request.query_params, or request.get_json()
• the sink is an outbound HTTP call such as:
  • requests.*
  • httpx.*
  • urllib.request.urlopen
  • aiohttp.ClientSession.*

It would also be useful if the plugin understood common safety guards, for example helpers like is_safe_url(url) or similar allowlist/private-address checks, so fixed code does not keep getting flagged.

Describe alternatives you've considered
A few alternatives came up:

• Relying on existing Bandit plugins: this does not solve the problem, because the current plugins around HTTP calls check hygiene issues, not untrusted URL flow.
• Keeping this in heavier dataflow tools only, such as CodeQL: that works, but Bandit is often used as a lightweight scanner, and this is a common enough pattern that it would be valuable to catch here too.
• A broader "untrusted data to external API" plugin: I tried that approach, but it produced noise by flagging non-network calls. Narrowing it to real network sinks worked much better.

The plugin prototype I implemented was inspired by CodeQL’s rule py/count-untrusted-data-external-api.
It works well on my projects, and it successfully detects CVE-2025-54381 and CVE-2025-61784 and avoids FPs in their fixed commits.

external-api-untrusted-data.zip

Start by reviewing the attached external-api-untrusted-data.zip prototype and the issue's listed request-controlled sources and outbound HTTP sinks. Compare its behavior with the existing Bandit HTTP plugins, then define tests for detecting the vulnerable flows while avoiding code protected by safety guards; done means the plugin reliably covers the described SSRF cases without the noted false positives.