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Security Policy

Supported Versions

We provide security updates for the following versions:

Latest minor version: Active support (e.g., v0.20.x)
Previous minor versions: Bug-fix support only
Versions older than 3 minor versions: No support

Example:

v0.20.0-0.20.x → Full support (latest)
v0.19.0-0.19.x → Bug-fix support only
v0.18.x and below → No support

Reporting a Vulnerability

Do not open a public issue or pull request for security vulnerabilities.

Instead, use GitHub Security Advisories to report privately, or email vpn.alpamys@gmail.com with:

Description: A clear explanation of the vulnerability
Steps to Reproduce: How to trigger or demonstrate the issue
Affected Versions: Which Soup versions are impacted
Suggested Fix (optional): Any proposed solutions
Contact Info: Your email for follow-up (optional)

What to Include

To: vpn.alpamys@gmail.com
Subject: Security Vulnerability Report: [Brief Title]

Description:
[Explain the vulnerability in detail]

Affected Component:
[e.g., data/loader.py, trainer/sft.py, etc.]

Steps to Reproduce:
1. [Step 1]
2. [Step 2]
3. ...

Impact:
[What could go wrong? Data exposure? RCE? DoS?]

Suggested Fix (optional):
[Your proposed solution, if any]

Response Timeline

Initial Response: Within 48 hours
Assessment: 1-3 business days
Fix Development: Varies by severity
Patch Release: As soon as possible after fix verification
Public Disclosure: Coordinated with reporter (typically 90 days after patch release)

Severity Levels

Critical: Remote code execution, data exposure, complete compromise (patch within 24-48 hours)
High: Authentication bypass, privilege escalation, denial of service (patch within 1 week)
Medium: Information disclosure, partial compromise (patch within 2 weeks)
Low: Minor issues with limited impact (patch in next regular release)

Security Best Practices

When using Soup, follow these practices to stay secure:

1. Keep Soup Updated

pip install --upgrade soup-cli

2. Protect API Keys

Never commit API keys or secrets to version control. Use environment variables:

export HUGGINGFACE_TOKEN=your_token_here
export WANDB_API_KEY=your_key_here
soup train

3. Validate Data

Only use trusted datasets
Verify checksums for large datasets
Inspect data for malicious content before training

4. Model Permissions

Be cautious when downloading models from untrusted sources
Use model hub providers with verified publishers (HuggingFace, Meta, etc.)
Keep track of which models you've fine-tuned and their base model sources

5. GPU/Compute Safety

Run on isolated machines if training on sensitive data
Clear cache and temporary files after training
Don't share fine-tuned models containing sensitive information

Known Vulnerabilities

We maintain a log of known security issues and their fixes. This will be updated as issues are discovered and resolved.

Current Status

No known critical vulnerabilities in current releases.

Security Hardening History

v0.10.10: Bearer token auth on Web UI, CORS restrictions, path traversal protection, SSRF prevention, max_tokens limits, supply-chain pinning (llama.cpp b5270), deprecated CLI secret flags
v0.12.0: experiment_name path traversal validation, GaLore parameter type enforcement
v0.13.0: Batch inference max_tokens capped at 16384, trust_remote_code warning
v0.14.0: Plaintext loader UTF-8 encoding, MoE config validation (moe_aux_loss_coeff ge=0, moe_lora boolean only)
v0.14.3: Data validate auto-detects format, Web UI --show-token flag + auth token documented
v0.15.0: rope_scaling_type Literal constraint, max_length bounds (ge=64, le=1048576), FSDP config key allowlist, Liger Kernel exception handling narrowed
v0.16.0: embedding_loss Literal constraint, embedding_margin gt=0 validation, ONNX export without trust_remote_code (with warning), TensorRT export subprocess list args (no shell injection), speculative decoding SSRF-protected (URL blocked) with warning panel, vLLM speculative model URL validation
v0.17.0: Server data generation provider SSRF validation (scheme whitelist + localhost-only HTTP), audio model trust_remote_code warning panel, audio file path traversal protection (resolved paths confined to audio_dir), SGLang backend trust_remote_code warning panel
v0.18.0: Ollama deploy GGUF path traversal protection + .gguf extension validation, model name validation (no path separators/null bytes), subprocess list args (no shell injection), Modelfile parameter key allowlist + value newline/null sanitization, overwrite warning panel
v0.19.0: Custom eval JSONL schema validation + 10k task cap, regex scoring ReDoS guard, judge API SSRF protection + API key isolation, human eval local-only terminal UI + 10k prompt cap, leaderboard read-only SQLite queries
v0.20.0: Ollama provider localhost-only validation (remote blocked), Anthropic provider API key from env only (never CLI arg), vLLM provider SSRF protection (scheme whitelist + localhost-only HTTP), output path traversal protection (.. blocked), configurable rate limiting (--requests-per-minute)

Security Scanning

All code is scanned with ruff for style and common issues
Dependencies are regularly updated to patch known CVEs
GitHub's dependency scanning alerts us to vulnerable dependencies
We use GitHub Actions CI/CD for continuous integration

Dependency Updates

We actively monitor and update dependencies:

Major dependency updates: Tested in PR before merging
Security patches: Applied immediately and released as patch versions
Deprecated dependencies: Replaced proactively

Coming Soon

Automated dependency scanning
SBOM (Software Bill of Materials) for each release
Third-party security audit (after 1.0.0 release)

Questions?

If you have security questions (not vulnerability reports) or need clarification:

Open a GitHub Discussion tagged security
Open a GitHub Issue (non-vulnerability inquiries)
Check our CONTRIBUTING.md for general support

License

This Security Policy is provided under the MIT license, same as the Soup project.

Last Updated: April 2026

For the latest version of this policy, visit: https://github.com/MakazhanAlpamys/Soup/blob/main/SECURITY.md

6.5 KiB Raw Blame History