ProtectSkills/MaliciousSkillBench
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How to use SkillScreen/SkillScreen-2B-SBR with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="SkillScreen/SkillScreen-2B-SBR") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForSequenceClassification
processor = AutoProcessor.from_pretrained("SkillScreen/SkillScreen-2B-SBR")
model = AutoModelForSequenceClassification.from_pretrained("SkillScreen/SkillScreen-2B-SBR", device_map="auto")2B decision model for detecting malicious agent skills from SKILL.md. Trained on MaliciousSkillBench using Source-Balanced Random (SBR), with an 8K input budget.
Based on OpenJev 2B. Full weights are at the root. The original LoRA adapter and classification head are in adapter/.
Requires CUDA with BF16 support.
pip install huggingface_hub
hf download SkillScreen/SkillScreen-2B-SBR --revision v1 --local-dir skillscreen
pip install -r skillscreen/requirements.txt
python skillscreen/score.py path/to/SKILL.md
Native maliciousness score ≥ 0.5 is flagged as suspicious.
Checkpoint details · Export verification · Project page · Code
Jerrold Huang, Junwei Quan, Saiyue Lyu, Zihan Guo, Yingqiang Ge.