base_model: openai-community/gpt2-xl
library_name: peft
license: apache-2.0
tags:
- cybersecurity
- ransomware-defense
- file-integrity
Phantester
Phantester is a GPT-2 XL LoRA adapter for classifying constrained, structured file-integrity telemetry and recommending defensive actions.
Intended use
Use only behind a deterministic policy engine. The model must not receive keys, perform cryptography, delete files, or directly trigger containment.
Limitations
The initial corpus is synthetic and does not establish real-world ransomware detection accuracy. GPT-2 XL is not instruction-tuned and has a limited context window. Any output outside the documented schema is untrusted.
Release gate
Do not publish a checkpoint until it reaches at least 95% exact-match accuracy and 100% recall for canary failures on a reviewed held-out set, while all cryptographic fail-closed tests pass.
Training and evaluation
The published adapter was trained for three epochs on 80,000 synthetic examples, validated on 10,000 examples, and evaluated on a disjoint 10,000 example test split. Identical prompts are confined to one split.
| Metric | Result |
|---|---|
| Exact JSON decision match | 100% |
| Canary-failure recall | 100% |
| Training loss | 0.7183 |
| Training runtime, H100 80GB | 20m 25s |
These results measure deterministic synthetic policy routing. They do not measure real-world ransomware detection and must not be interpreted as a production false-positive or false-negative rate.