CVE exploitability model: weights + preprocessor + loader + card

README.md

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+---
+license: mit
+library_name: pytorch
+pipeline_tag: text-classification
+tags:
+- security
+- cve
+- vulnerability-management
+- exploit-prediction
+- sbom
+---
+
+# CVE Exploitability Model
+
+Predicts the probability that a CVE is **exploited in the wild** (CISA KEV as the positive label),
+for prioritising vulnerabilities surfaced from an SBOM. Two-branch deep model: a **TextCNN** over the
+CVE description fused with an **MLP** over CVSS v3 metadata + CWE.
+
+## Held-out test metrics
+| Model | ROC-AUC | PR-AUC | Precision@10% | Recall@10% |
+|---|---|---|---|---|
+| Deep (TextCNN + CVSS fusion) | 0.934 | 0.763 | 0.782 | 0.473 |
+| Baseline: CVSS score only | 0.746 | 0.316 | 0.347 | 0.210 |
+| Baseline: logistic reg (structured) | 0.819 | 0.440 | 0.463 | 0.280 |
+
+## Usage
+```python
+# pip install huggingface_hub torch numpy pandas
+import importlib.util, sys
+from huggingface_hub import hf_hub_download
+spec = importlib.util.spec_from_file_location("hf_model", hf_hub_download("sumitp76/cve-exploitability", "hf_model.py"))
+m = importlib.util.module_from_spec(spec); sys.modules["hf_model"] = m; spec.loader.exec_module(m)
+
+pp, net = m.load_model("sumitp76/cve-exploitability")
+
+import pandas as pd, torch
+df = pd.DataFrame([{"description": "Remote code execution via crafted request ...",
+                     "base_score": 9.8, "severity": "CRITICAL", "AV": "NETWORK", "AC": "LOW",
+                     "PR": "NONE", "UI": "NONE", "S": "UNCHANGED", "C": "HIGH", "I": "HIGH",
+                     "A": "HIGH", "has_v3": 1, "v2_base_score": None, "year": 2024, "cwe": "CWE-94"}])
+Xt = torch.tensor(pp.transform_text(df.description.tolist()))
+Xs = torch.tensor(pp.transform_struct(df), dtype=torch.float32)
+print("exploit probability:", torch.sigmoid(net(Xt, Xs)).item())
+```
+
+## Labels
+- `1` = listed in CISA KEV (known exploited in the wild)
+- `0` = not known-exploited (sampled from NVD)
+
+## Data sources
+- **Labels:** CISA KEV (`cisagov/kev-data`)
+- **Features:** NVD (`fkie-cad/nvd-json-data-feeds`) — description, CVSS v3, CWE
+
+## Limitations
+KEV is a weak/incomplete label; the training class prevalence is inflated versus the real-world
+(<1%); CVEs seen during training score optimistically; novel patterns (e.g. supply-chain backdoors)
+are hard. Evaluate with a time-based split before operational use. This model assists triage and is
+not a substitute for human judgement.

config.json

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+{
+  "architecture": "ExploitabilityNet (TextCNN + structured MLP fusion)",
+  "framework": "pytorch",
+  "task": "binary-classification",
+  "max_len": 200,
+  "vocab_size": 20000,
+  "struct_dim": 80,
+  "labels": {
+    "0": "not-known-exploited",
+    "1": "known-exploited (CISA KEV)"
+  },
+  "test_metrics": {
+    "roc_auc": 0.9337318710296056,
+    "pr_auc": 0.7629452988233197,
+    "precision_at_10pct": 0.7823129296302795,
+    "recall_at_10pct": 0.47325101494789124,
+    "positive_rate": 0.16430020332336426
+  }
+}

hf_model.py

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+# Self-contained loader for the CVE exploitability model.
+# Usage:
+#   from huggingface_hub import hf_hub_download
+#   import importlib.util, sys
+#   spec = importlib.util.spec_from_file_location("hf_model", hf_hub_download("sumitp76/cve-exploitability","hf_model.py"))
+#   m = importlib.util.module_from_spec(spec); sys.modules["hf_model"]=m; spec.loader.exec_module(m)
+#   pp, net = m.load_model("sumitp76/cve-exploitability")
+import re, json
+import numpy as np
+import torch, torch.nn as nn
+
+_WORD = re.compile(r"[A-Za-z0-9_.\-]+")
+CAT_COLS = ["severity","AV","AC","PR","UI","S","C","I","A"]
+NUM_COLS = ["base_score","v2_base_score","has_v3","year","desc_len"]
+
+def tokenize(t): return _WORD.findall(str(t).lower())
+def _isnan(x):
+    try: return isinstance(x, float) and np.isnan(x)
+    except Exception: return False
+
+class Preprocessor:
+    def __init__(self, max_len=200, max_vocab=20000):
+        self.max_len=max_len; self.max_vocab=max_vocab
+        self.word2idx={}; self.cat_maps={}; self.cwe_vocab={}
+        self.num_mean={}; self.num_std={}
+    def transform_text(self, descriptions):
+        X = np.zeros((len(descriptions), self.max_len), dtype=np.int64)
+        for i,t in enumerate(descriptions):
+            for j,w in enumerate(tokenize(t)[:self.max_len]):
+                X[i,j] = self.word2idx.get(w,1)
+        return X
+    def transform_struct(self, df):
+        n=len(df)
+        def col(name, default):
+            return df[name].values if name in df.columns else np.array([default]*n)
+        num=np.zeros((n,len(NUM_COLS)),dtype=np.float32)
+        desc_len=np.array([len(str(x)) for x in col("description","")],dtype=float)
+        srcs={"base_score":col("base_score",np.nan).astype(float),
+              "v2_base_score":col("v2_base_score",np.nan).astype(float),
+              "has_v3":col("has_v3",0).astype(float),
+              "year":col("year",2020).astype(float),
+              "desc_len":desc_len}
+        for k,name in enumerate(NUM_COLS):
+            v=srcs[name].astype(float); v=np.where(np.isnan(v), self.num_mean[name], v)
+            num[:,k]=(v-self.num_mean[name])/self.num_std[name]
+        blocks=[num]
+        for c in CAT_COLS:
+            m=self.cat_maps[c]; width=len(m)+2; b=np.zeros((n,width),dtype=np.float32)
+            for i,val in enumerate(col(c,None)):
+                idx = m.get(val,1) if (val is not None and not _isnan(val)) else 0
+                b[i,idx]=1.0
+            blocks.append(b)
+        cwe_w=len(self.cwe_vocab)+1; cb=np.zeros((n,cwe_w),dtype=np.float32)
+        for i,val in enumerate(col("cwe","UNKNOWN")): cb[i,self.cwe_vocab.get(val,0)]=1.0
+        blocks.append(cb)
+        return np.concatenate(blocks, axis=1)
+    @property
+    def struct_dim(self):
+        return len(NUM_COLS)+sum(len(self.cat_maps[c])+2 for c in CAT_COLS)+len(self.cwe_vocab)+1
+    @property
+    def vocab_size(self): return len(self.word2idx)
+    @classmethod
+    def from_dict(cls,d):
+        p=cls(d["max_len"],d["max_vocab"]); p.word2idx=d["word2idx"]; p.cat_maps=d["cat_maps"]
+        p.cwe_vocab=d["cwe_vocab"]; p.num_mean=d["num_mean"]; p.num_std=d["num_std"]; return p
+
+class TextCNN(nn.Module):
+    def __init__(self, vocab_size, emb_dim=128, kernels=(2,3,4,5), n_filters=96, dropout=0.3, out_dim=192):
+        super().__init__()
+        self.emb=nn.Embedding(vocab_size, emb_dim, padding_idx=0)
+        self.convs=nn.ModuleList(nn.Conv1d(emb_dim,n_filters,k,padding=k//2) for k in kernels)
+        self.act=nn.ReLU(); self.drop=nn.Dropout(dropout)
+        self.proj=nn.Linear(n_filters*len(kernels), out_dim); self.out_dim=out_dim
+    def forward(self,x):
+        e=self.emb(x).transpose(1,2)
+        feats=[self.act(c(e)).max(dim=2).values for c in self.convs]
+        return self.proj(self.drop(torch.cat(feats,dim=1)))
+
+class StructuredEncoder(nn.Module):
+    def __init__(self, in_dim, hidden=128, out_dim=96, dropout=0.3):
+        super().__init__()
+        self.net=nn.Sequential(nn.Linear(in_dim,hidden),nn.ReLU(),nn.Dropout(dropout),
+                               nn.Linear(hidden,out_dim),nn.ReLU()); self.out_dim=out_dim
+    def forward(self,x): return self.net(x)
+
+class ExploitabilityNet(nn.Module):
+    def __init__(self, vocab_size, struct_dim, dropout=0.3):
+        super().__init__()
+        self.text=TextCNN(vocab_size,dropout=dropout)
+        self.struct=StructuredEncoder(struct_dim,dropout=dropout)
+        self.head=nn.Sequential(nn.Linear(self.text.out_dim+self.struct.out_dim,128),
+                                nn.ReLU(),nn.Dropout(dropout),nn.Linear(128,1))
+    def forward(self, text_ids, struct):
+        return self.head(torch.cat([self.text(text_ids), self.struct(struct)],dim=1)).squeeze(-1)
+
+def load_model(repo_id, device="cpu"):
+    from huggingface_hub import hf_hub_download
+    pp = Preprocessor.from_dict(json.load(open(hf_hub_download(repo_id,"preprocessor.json"))))
+    net = ExploitabilityNet(pp.vocab_size, pp.struct_dim).to(device)
+    net.load_state_dict(torch.load(hf_hub_download(repo_id,"model.pt"), map_location=device))
+    net.eval()
+    return pp, net

metrics.json

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+{
+  "deep_model": {
+    "roc_auc": 0.9337318710296056,
+    "pr_auc": 0.7629452988233197,
+    "precision_at_10pct": 0.7823129296302795,
+    "recall_at_10pct": 0.47325101494789124,
+    "positive_rate": 0.16430020332336426
+  },
+  "baselines": {
+    "cvss_base_score_only": {
+      "roc_auc": 0.7457815600570006,
+      "pr_auc": 0.31563649299889174,
+      "precision_at_10pct": 0.3469387888908386,
+      "recall_at_10pct": 0.20987653732299805,
+      "positive_rate": 0.16430020332336426
+    },
+    "logreg_structured_only": {
+      "roc_auc": 0.8193495545167605,
+      "pr_auc": 0.4403080599956068,
+      "precision_at_10pct": 0.4625850319862366,
+      "recall_at_10pct": 0.27983540296554565,
+      "positive_rate": 0.16430020332336426
+    }
+  }
+}

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:06ab1eebdb32ad2885d697f1b06ce982029b98f6aaa02596c01b33b04817b757
+size 11470776

test_predictions.csv

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+cve_id,label,exploit_prob
+CVE-2025-24085,1.0,1.0
+CVE-2017-6740,1.0,1.0
+CVE-2014-4114,1.0,1.0
+CVE-2016-7855,1.0,1.0
+CVE-2015-1701,1.0,1.0
+CVE-2012-4969,1.0,1.0
+CVE-2014-0502,1.0,1.0
+CVE-2017-5638,1.0,0.9995117
+CVE-2014-4077,1.0,0.9995117
+CVE-2015-7645,1.0,0.9995117
+CVE-2016-4171,1.0,0.9995117
+CVE-2023-32373,1.0,0.9995117
+CVE-2021-30761,1.0,0.9995117
+CVE-2013-0632,1.0,0.9995117
+CVE-2025-59287,1.0,0.9995117
+CVE-2025-5086,1.0,0.9995117
+CVE-2011-0611,1.0,0.9995117
+CVE-2025-26399,1.0,0.99902344
+CVE-2015-2387,1.0,0.99902344
+CVE-2021-34523,1.0,0.99853516
+CVE-2016-0099,1.0,0.99853516
+CVE-2021-20124,1.0,0.99853516
+CVE-2023-26359,1.0,0.99853516
+CVE-2025-31200,1.0,0.9975586
+CVE-2021-30860,1.0,0.9975586
+CVE-2022-40684,1.0,0.9970703
+CVE-2019-7195,1.0,0.9970703
+CVE-2016-0983,0.0,0.9970703
+CVE-2017-0145,1.0,0.9970703
+CVE-2025-40551,1.0,0.99658203
+CVE-2025-24200,1.0,0.99609375
+CVE-2026-10520,1.0,0.99609375
+CVE-2020-4428,1.0,0.99609375
+CVE-2016-3343,0.0,0.99560547
+CVE-2012-5076,1.0,0.99560547
+CVE-2017-7269,1.0,0.9946289
+CVE-2016-0040,1.0,0.9941406
+CVE-2020-1020,1.0,0.99365234
+CVE-2013-3893,1.0,0.99316406
+CVE-2024-0012,1.0,0.9926758
+CVE-2025-30406,1.0,0.9921875
+CVE-2020-27932,1.0,0.9916992
+CVE-2023-41064,1.0,0.99121094
+CVE-2022-23227,1.0,0.99072266
+CVE-2013-2551,1.0,0.9902344
+CVE-2025-2747,1.0,0.9892578
+CVE-2018-8373,1.0,0.9892578
+CVE-2009-0557,1.0,0.98828125
+CVE-2018-6809,0.0,0.98828125
+CVE-2024-37079,1.0,0.98828125
+CVE-2023-2136,1.0,0.9868164
+CVE-2018-8453,1.0,0.9863281
+CVE-2009-3129,1.0,0.98535156
+CVE-2023-36424,1.0,0.9848633
+CVE-2023-37580,1.0,0.9848633
+CVE-2025-20281,1.0,0.98339844
+CVE-2022-41091,1.0,0.98291016
+CVE-2025-2775,1.0,0.98291016
+CVE-2025-54948,1.0,0.9824219
+CVE-2024-38193,1.0,0.9824219
+CVE-2021-28663,1.0,0.9819336
+CVE-2017-8540,1.0,0.9814453
+CVE-2022-20866,0.0,0.98046875
+CVE-2025-54253,1.0,0.97998047
+CVE-2023-21739,0.0,0.9794922
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+CVE-2018-0175,1.0,0.9008789
+CVE-2014-0496,1.0,0.9003906
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