CIRCL
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vulnerability-attack-technique-biencoder

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 library_name: transformers
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 ---
+license: gpl-3.0
+base_model: FacebookAI/roberta-base
+datasets:
+  - CIRCL/vulnerability-attack-techniques
+language:
+  - en
+tags:
+  - security
+  - vulnerability
+  - mitre-attack
+  - cve
+  - bi-encoder
+  - sentence-similarity
 library_name: transformers
 ---
+# vulnerability-attack-technique-biencoder
+A label-semantics bi-encoder that suggests MITRE ATT&CK (Enterprise)
+techniques for a CVE by scoring the vulnerability description against the
+**official ATT&CK technique descriptions** in a shared embedding space.
+Unlike the companion classification head
+([`CIRCL/vulnerability-attack-technique-classification-roberta-base`](https://huggingface.co/CIRCL/vulnerability-attack-technique-classification-roberta-base)),
+it can rank *any* technique that has an official description — the label
+is text, not a learned output row.
+One shared `roberta-base` encoder embeds both the CVE text
+(title + description) and each technique's STIX name+description
+(citation markup stripped, 256 tokens), mean-pooled and L2-normalized;
+the score is a learned affine over the cosine. Trained on the curated
+gold set [`CIRCL/vulnerability-attack-techniques`](https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques)
+(~1,200 CVEs, CTID methodology) with per-label-weighted BCE over a
+53-parent-technique vocabulary, with VulnTrain
+(`vulntrain-train-attack-biencoder`).
+## When to use which model
+- **Classification head**: best top-5 ranking on the trained vocabulary
+  (recall@5 0.667 ± 0.015 across five seeds).
+- **This bi-encoder**: slightly lower recall@5 (0.643 ± 0.019) but the
+  largest consistent rare-technique gain measured on this task
+  (macro-F1 0.212 ± 0.011 vs 0.176 ± 0.016, +21% relative), and
+  open-vocabulary ranking over all 222 active parent techniques
+  (recall@5 0.515 ± 0.020, 2.3× a generic zero-shot sentence embedder).
+Caveat measured in the accompanying paper: zero-shot ranking of
+techniques *absent from training* does **not** benefit from this
+fine-tuning — in a five-fold label-holdout evaluation the fine-tuned
+encoder ranked held-out techniques below a generic MiniLM embedder.
+Rankings for techniques outside the 53-technique training vocabulary
+should be treated as no better than generic semantic similarity.
+## Usage
+The repository ships `technique_texts.json` (the exact technique texts
+used at training time) and the scoring calibration in
+`config.biencoder`:
+```python
+import json, torch
+from huggingface_hub import hf_hub_download
+from transformers import AutoModel, AutoTokenizer
+model_id = "CIRCL/vulnerability-attack-technique-biencoder"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+encoder = AutoModel.from_pretrained(model_id).eval()
+cfg = encoder.config.biencoder
+texts = json.load(open(hf_hub_download(model_id, "technique_texts.json")))
+def embed(batch, max_length=512):
+    enc = tokenizer(batch, padding=True, truncation=True,
+                    max_length=max_length, return_tensors="pt")
+    hidden = encoder(**enc).last_hidden_state
+    mask = enc["attention_mask"].unsqueeze(-1)
+    pooled = (hidden * mask).sum(1) / mask.sum(1)
+    return torch.nn.functional.normalize(pooled, dim=-1)
+techniques = sorted(texts)
+with torch.no_grad():
+    technique_emb = embed([texts[t] for t in techniques],
+                          cfg["technique_max_length"])
+    cve_emb = embed(["Improper neutralization of special elements used "
+                     "in an OS command in the web management interface..."])
+scores = cfg["logit_scale"] * (cve_emb @ technique_emb.T) + cfg["logit_bias"]
+for idx in scores[0].topk(5).indices:
+    print(techniques[idx], float(scores[0][idx]))
+```
+Evaluation and stratified breakdowns are reproducible with
+`vulntrain-validate-attack-classification --method biencoder --model
+CIRCL/vulnerability-attack-technique-biencoder` (add `--candidates full`
+for open-vocabulary ranking over all active parent techniques).
+## Intended use and limitations
+The model generates **candidate techniques for analyst review**, not
+authoritative mappings. Technique-to-CVE mapping involves analyst
+judgment; the training labels inherit the CTID methodology's
+subjectivity, and the gold set over-represents exploited and enriched
+CVEs. English descriptions only; parent-level techniques only.
+## References
+- Bonhomme, C., & Dulaunoy, A. (2026). *Mapping CVEs to MITRE ATT&CK
+  Techniques: A Curated Gold-Set Classifier and the Limits of
+  LLM-Assisted Label Expansion.* [arXiv:2607.25572](https://arxiv.org/abs/2607.25572)
+- Bonhomme, C., & Dulaunoy, A. (2026). *Beyond the Description:
+  Structured Metadata and Label Semantics for CVE-to-ATT&CK Mapping.*
+  (follow-up paper, in preparation — source of all numbers above)
+- Trained with [VulnTrain](https://github.com/vulnerability-lookup/VulnTrain)
+  as part of the [Vulnerability-Lookup](https://vulnerability.circl.lu) project.