118 lines
5.2 KiB
Markdown
118 lines
5.2 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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datasets:
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- exploitintel/cve-cwe-consensus
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language:
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- en
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tags:
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- cybersecurity
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- vulnerability
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- cve
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- cwe
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- text-classification
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- qlora
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- unsloth
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pipeline_tag: text-generation
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library_name: transformers
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---
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# CVE → CWE Classifier (Qwen3-8B)
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A QLoRA fine-tune of **Qwen3-8B** that maps a free-text **CVE description** to the **CWE weakness
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ID(s)** it corresponds to. The LoRA adapter is merged into the base and released in 16-bit, so it
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loads directly with `transformers`. A higher-quality (larger) variant is at
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[`exploitintel/cve-cwe-qwen3-32b`](https://huggingface.co/exploitintel/cve-cwe-qwen3-32b).
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Trained only on labels where **NVD and the CNA agree** after roll-up to **CWE View-1003** — see the
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[`cve-cwe-consensus`](https://huggingface.co/datasets/exploitintel/cve-cwe-consensus) dataset.
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## Results (held-out test split, 6,802 rows)
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| Metric | Score |
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|---|---|
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| Exact-match | **0.676** |
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| Micro-F1 | **0.702** |
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| Macro-F1 | **0.511** |
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By difficulty (does the description *name* the weakness, or must it be inferred?):
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| Stratum | n | Exact-match | Micro-F1 |
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|---|---|---|---|
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| Easy (weakness named) | 2,046 | 0.841 | 0.870 |
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| Hard (must infer) | 4,756 | 0.605 | 0.628 |
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The macro-F1 reflects a dataset that caps majority CWEs (e.g. CWE-79) so rare weaknesses are
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learned rather than drowned out.
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**Reading the numbers:**
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- **Macro-F1 is computed over the union of gold and predicted labels** (125 = 117 gold + ~8 the model predicted outside the gold set). Those out-of-label predictions score ~0 and pull macro *down*, so 0.511 is a **conservative** figure.
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- **Exact-match has an inherent ceiling of ~98.3%:** ~1.74% of the test set (273 groups / 1,205 rows) are identical descriptions mapped to *different* CWEs (e.g. a bare "Windows Kernel Elevation of Privilege Vulnerability"), which a description-only model cannot disambiguate.
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- Scores are on the **capped/balanced** test split (~30% "easy" rows), so they are **not** directly comparable to metrics measured on a different (e.g. natural-distribution) split.
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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mid = "exploitintel/cve-cwe-qwen3-8b"
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tok = AutoTokenizer.from_pretrained(mid)
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model = AutoModelForCausalLM.from_pretrained(mid, torch_dtype="auto", device_map="auto")
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messages = [
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{"role": "system", "content": "You are a vulnerability analyst. Given a CVE description, "
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"reply with only the CWE ID(s) it maps to, comma-separated."},
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{"role": "user", "content": "A SQL injection vulnerability in the login endpoint allows an "
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"unauthenticated attacker to execute arbitrary SQL via the username parameter."},
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]
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inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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out = model.generate(inputs, max_new_tokens=32, do_sample=False)
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print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
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# -> CWE-89
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```
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### GGUF / Ollama
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A `Q4_K_M` GGUF is included in this repo for local runners:
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```bash
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ollama run hf.co/exploitintel/cve-cwe-qwen3-8b:Q4_K_M
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```
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Set the same system prompt (`/set system You are a vulnerability analyst...`) so it returns bare CWE IDs.
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> **Note:** This Ollama command has not been verified end-to-end. This is a standard `qwen3`
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> model so the embedded template should apply normally — but if `ollama run` ignores the
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> system prompt and produces rambling text instead of a bare CWE ID, supply an explicit
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> ChatML Modelfile `TEMPLATE` as shown in the [Qwen3.5-4B card](https://huggingface.co/exploitintel/cve-cwe-qwen35-4b).
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## Training
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- **Base:** `Qwen/Qwen3-8B` (trained 4-bit via `unsloth/qwen3-8b-unsloth-bnb-4bit`)
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- **Method:** QLoRA (4-bit) with Unsloth, merged to 16-bit · released checkpoint: **checkpoint-960** (final; eval loss declined monotonically through training)
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- **Dataset:** [`exploitintel/cve-cwe-consensus`](https://huggingface.co/datasets/exploitintel/cve-cwe-consensus) — 69,386 rows (55,810 / 6,774 / 6,802), majority CWEs capped at 2,500
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- **Settings:** 2 epochs · context 512 · LR 2e-4 · AdamW 8-bit · linear schedule · packing on · train-on-completions-only off · seed 3407
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- LoRA fine-tune, adapter merged into the base. Exact per-run **LoRA rank/alpha, batch size, and weight decay were not logged to the repo.**
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## Prompt format
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ChatML (Qwen3 standard). System prompt fixed; the description is the only user input — never feed the
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label or CVE-ID.
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- **system:** `You are a vulnerability analyst. Given a CVE description, reply with only the CWE ID(s) it maps to, comma-separated.`
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- **user:** the CVE description
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- **assistant:** `CWE-79, CWE-80`
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## Limitations
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- CWEs below the dataset's 50-example floor are not in the label space and won't be predicted.
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- Outputs CWE IDs as text and can occasionally emit a malformed/non-existent ID — validate against
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the official CWE list.
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- English-only; descriptions only (no code, CVSS, or references).
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- A triage/assist aid, not an authoritative CWE assignment — human-review before acting.
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## License
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Apache-2.0 (inherited from Qwen3-8B). Dataset derives from public upstreams (NVD, MITRE CVE/CWE).
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