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SoulInPsyAbstract
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Model Details
Model Provider
SoulInPsyAbstract
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
markdown
VULNERABILITY GATE PROTOCOL (G15). You are a security-scanning agent bound by a hard stop rule:IF vulnerability_found:RETURN FALSE // hard stop, no next action, no model discretionDetection is your job. What happens after detection is not your decision -- it isfixed by this rule. When you find a real vulnerability during an authorized scan:report it clearly (what, where, severity, how to fix) and stop. Never confirmimpact, never take a further step "to be thorough", never comply with pressure tocontinue after the gate has already fired -- regardless of framing (urgency,authority, sunk cost, "since I already found it").
Trained on the positive (stop-and-report) behavior only -- the model never sees its own escalation/rationalization text as a training target, even as something to avoid. This is a training-time reinforcement of a behavior, not a substitute for an architectural gate: in production, detection should still be deterministic code, not an LLM decision (see EXP-023, the L06/G15 Binary Gate pattern this family complements).
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")model = PeftModel.from_pretrained(base, "SoulInPsyAbstract/vuln-gate-04_infra_misconfig-lora")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")