# AutoRL-Bench Dependencies
# conda environment: Python 3.10

# RL Training (core)
trl>=0.27.0
accelerate>=1.0.0
datasets>=3.0.0
peft>=0.18.1

# Evaluation
opencompass==0.5.1
setuptools>=78.1.1  # Security fix: GHSA-8g6x-3r52-4m6c (path traversal in PackageIndex.download, arbitrary file write/RCE)

# Inference acceleration (optional, TRL supports 0.10.2-0.12.0)
# Security: Version >=0.14.0 fixes CVE-2026-22807 (RCE via auto_map dynamic module loading)
# Security: Version >=0.19.0 fixes:
#   - CVE-2026-34753: SSRF in download_bytes_from_url
#   - CVE-2026-34756: OOM DoS via unbounded 'n' parameter
#   - CVE-2026-34755: OOM DoS via unbounded video/jpeg frame count
#   - CVE-2026-22778: RCE via JPEG2000 heap overflow
#   - CVE-2026-27893
# Current spec (>=0.19.0) is already safe. Dependabot alerts are false positives due to missing lockfile.
vllm>=0.19.0

# Data processing
numpy>=1.26.0
pandas>=1.5.0
pydantic>=2.4.0  # Security fix: CVE-2024-3772 (ReDoS via crafted email)

# Models
# Security: transformers >=4.51.0 fixes CVE-2025-3263 (ReDoS in get_configuration_file)
# Security: transformers >=4.50.0 fixes CVE-2025-1194 (ReDoS in GPT-NeoX-Japanese tokenizer)
# Security: transformers >=4.52.1 fixes CVE-2025-3777 (URL validation bypass via username injection)
# Security: transformers >=5.0.0rc3 fixes CVE-2026-1839 (RCE via Trainer._load_rng_state without weights_only=True)
# Current spec (>=5.0.0rc3) is already safe. Dependabot alerts are false positives due to missing lockfile.
# Security: torch >=2.7.1 fixes CVE-2025-2953 (DoS in mkldnn_max_pool2d) - current spec >=2.8.0 is safe
torch>=2.8.0  # Security fix: CVE-2025-32434 (torch.load RCE), CVE-2025-3730 (DoS in ctc_loss), CVE-2025-2953 (DoS in mkldnn_max_pool2d)
transformers>=5.0.0rc3  # Security fix: CVE-2024-11393 (RCE), CVE-2025-3264/3933/2099/6051/1194/6638 (ReDoS), CVE-2025-3777 (URL validation), CVE-2026-1839 (RCE in Trainer)
huggingface_hub>=0.20.0

# Web services
flask
flask-cors

# Tools
loguru
requests
pyyaml
