Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/ivangrynenko/cursorrulesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/ivangrynenko/cursorrules/python-ssrf)<a href="https://agentmods.dev/rules/ivangrynenko/cursorrules/python-ssrf"><img src="https://agentmods.dev/badge/rules/ivangrynenko/cursorrules/python-ssrf/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/ivangrynenko/cursorrules/python-ssrf"><img src="https://agentmods.dev/badge/rules/ivangrynenko/cursorrules/python-ssrf.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.05124 |
| Opus 5.5 | $0.00000 | $0.02050 |
| Sonnet 5.5 | $0.00000 | $0.01025 |
| Haiku 4.5 | $0.00000 | $0.00512 |
Grade A, and why
python-ssrf scanned grade A with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
# Pattern 2: Detect urllib usage with user input Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
# Pattern 8: Detect os.system calls with user input that might lead to SSRF Copies of this mod
1 near-identical copy found in the catalogue:
- python-ssrf — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 520 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Server-Side Request Forgery (SSRF) Standards (OWASP A10:2021)
This rule enforces security best practices to prevent Server-Side Request Forgery (SSRF) vulnerabilities in Python applications, as defined in OWASP Top 10:2021-A10.
actions:
-
type: enforce conditions:
Pattern 1: Detect direct use of requests library with user input
- pattern: "requests\.(get|post|put|delete|head|options|patch)\([^)]*?\b(request\.\w+|params\[\'[^\']+\'\]|data\[\'[^\']+\'\]|json\[\'[^\']+\'\]|args\.get|form\.get)" message: "Potential SSRF vulnerability detected. User-controlled input is being used directly in HTTP requests. Implement URL validation and allowlisting."
Pattern 2: Detect urllib usage with user input
- pattern: "urllib\.(request|parse)\.\w+\([^)]*?\b(request\.\w+|params\[\'[^\']+\'\]|data\[\'[^\']+\'\]|json\[\'[^\']+\'\]|args\.get|form\.get)" message: "Potential SSRF vulnerability detected. User-controlled input is being used directly in urllib functions. Implement URL validation and allowlisting."
Pattern 3: Detect http.client usage with user input
- pattern: "http\.client\.\w+\([^)]*?\b(request\.\w+|params\[\'[^\']+\'\]|data\[\'[^\']+\'\]|json\[\'[^\']+\'\]|args\.get|form\.get)" message: "Potential SSRF vulnerability detected. User-controlled input is being used directly in http.client functions. Implement URL validation and allowlisting."
Pattern 4: Detect aiohttp usage with user input
- pattern: "aiohttp\.\w+\([^)]*?\b(request\.\w+|params\[\'[^\']+\'\]|data\[\'[^\']+\'\]|json\[\'[^\']+\'\]|args\.get|form\.get)" message: "Potential SSRF vulnerability detected. User-controlled input is being used directly in aiohttp functions. Implement URL validation and allowlisting."
Pattern 5: Detect httpx usage with user input
- pattern: "httpx\.\w+\([^)]*?\b(request\.\w+|params\[\'[^\']+\'\]|data\[\'[^\']+\'\]|json\[\'[^\']+\'\]|args\.get|form\.get)" message: "Potential SSRF vulnerability detected. User-controlled input is being used directly in httpx functions. Implement URL validation and allowlisting."
Pattern 6: Detect pycurl usage with user input
- pattern: "pycurl\.\w+\([^)]*?\b(request\.\w+|params\[\'[^\']+\'\]|data\[\'[^\']+\'\]|json\[\'[^\']+\'\]|args\.get|form\.get)" message: "Potential SSRF vulnerability detected. User-controlled input is being used directly in pycurl functions. Implement URL validation and allowlisting."
Pattern 7: Detect subprocess calls with user input that might lead to SSRF
- pattern: "subprocess\.(Popen|call|run|check_output|check_call)\([^)]*?\b(request\.\w+|params\[\'[^\']+\'\]|data\[\'[^\']+\'\]|json\[\'[^\']+\'\]|args\.get|form\.get)" message: "Potential SSRF vulnerability detected. User-controlled input is being used in subprocess calls, which might lead to SSRF. Validate and sanitize input."
Pattern 8: Detect os.system calls with user input that might lead to SSRF
- pattern: "os\.(system|popen|spawn)\([^)]*?\b(request\.\w+|params\[\'[^\']+\'\]|data\[\'[^\']+\'\]|json\[\'[^\']+\'\]|args\.get|form\.get)" message: "Potential SSRF vulnerability detected. User-controlled input is being used in OS commands, which might lead to SSRF. Validate and sanitize input."
Pattern 9: Detect URL construction with user input
- pattern: "(f|r)["']https?://[^"']?\{[^\}]?\b(request\.\w+|params\[\'[^\']+\'\]|data\[\'[^\']+\'\]|json\[\'[^\']+\'\]|args\.get|form\.get)" message: "Potential SSRF vulnerability detected. User-controlled input is being used in URL construction. Implement URL validation and allowlisting."
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 520 lines · 5,124 tokens per session scan A 530902b85b21
python-ssrf is a cursor rule published in the GitHub repository ivangrynenko/cursorrules (88 stars, last pushed 11mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 5,124 tokens. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other cursor rules, from other repositories
django
Django: models, views, ORM best practices.
python
Python best practices: type hints, pathlib, pytest, clean error handling.
pydantic
Pydantic: BaseModel, validators, Field, Settings.
pytest
You are an expert in pytest testing. Follow these rules.
pytorch
PyTorch: neural networks, model training, GPU optimization.
ruff
Ruff: linting, formatting, rule selection, configuration.