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README.md

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-# Do Language Models Prefer Vulnerable Code? A Probabilistic Study of Insecure Code Preference
+# DeltaSecommits: Real-World Vulnerability-Fixing Commits
+from **Do Language Models Prefer Vulnerable Code? A Probabilistic Study of Insecure Code Preference** @ICST 2026
+
+A curated dataset of **2,493 paired vulnerable/secure C code samples** from real-world vulnerability-fixing commits, covering **25 CWE (Common Weakness Enumeration) categories** and **2,422 unique vulnerability identifiers**.
+
+## Overview
+
+DeltaSecommits provides paired code snapshots—the vulnerable version (pre-patch) and the secure version (post-patch) of the same file from a single commit. This structure eliminates confounds from stylistic variation between authors and projects, making it ideal for mechanistic analysis of how models encode security properties.
+
+Each pair includes:
+- **Vulnerable code**: Pre-patch version with the security flaw
+- **Secure code**: Post-patch version with the fix applied
+- **CWE identifier**: The specific vulnerability type (CWE-119, CWE-89, etc.)
+- **Severity rating**: CVE severity (if available)
+
+## Data Source & Curation
+
+The dataset is drawn from authoritative sources:
+- **Open Source Vulnerability (OSV) Database**: https://osv.dev
+- **National Vulnerability Database (NVD)**: https://nvd.nist.gov
+
+**Filtering pipeline** (for quality assurance):
+1. Single-file commits only — excludes multi-file changes that cloud the signal
+2. Single-commit fixes — removes vulnerabilities requiring multiple commits to resolve
+3. Patch retrieval — uses GitHub API to fetch commit metadata and validate patches
+4. Label deduplication — removes duplicate vulnerability IDs
+
+This resulted in the final dataset of **2,493 high-quality pairs** from **278 publicly disclosed vulnerabilities**.
+
+## CWE Coverage
+
+The dataset spans 25 CWE categories across four major families:
+
+**Memory Safety** (3 CWEs, 610 pairs)
+- CWE-119: Buffer Overflow
+- CWE-125: Out-of-bounds Read
+- CWE-787: Out-of-bounds Write
+
+**Injection** (2 CWEs, 471 pairs)
+- CWE-89: SQL Injection
+- CWE-79: Cross-Site Scripting (XSS)
+
+**Resource Management** (3 CWEs, 248 pairs)
+- CWE-401: Memory Leak
+- CWE-400: Resource Consumption
+- CWE-416: Use-After-Free
+
+**Information Disclosure** (1 CWE, 143 pairs)
+- CWE-200: Sensitive Information Exposure
+
+And 16 additional CWEs with varying sample counts (minimum 20 pairs per type).
+
+## Dataset Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total pairs | 2,493 |
+| CWE categories | 25 |
+| Unique vulnerabilities | 2,422 |
+| Programming language | C |
+| Mean tokens (vulnerable) | 230 |
+| Mean tokens (secure) | 191 |
+| Pairs where vulnerable > secure | 85% |
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load full dataset
+ds = load_dataset("deltasecommits")
+
+# Access a single example
+example = ds["train"][0]
+print(example["vulnerable_code"])
+print(example["secure_code"])
+print(example["cwe"])
+```
+
+### Data Format
+
+Each example contains:
+```python
+{
+    "vulnerable_code": str,      # Pre-patch code with vulnerability
+    "secure_code": str,          # Post-patch code with fix
+    "cwe": str,                  # CWE identifier (e.g., "CWE-119")
+    "severity": str,             # CVE severity (if available)
+    "vulnerability_id": str,     # Unique identifier
+    "source": str                # Source (osv, nvd)
+}
+```
+
+
+## Use Cases
+
+**Security-focused ML research:**
+- Training vulnerability detection models
+- Analyzing how LLMs encode security information
+- Benchmarking code security analyzers
+
+**Mechanistic interpretability:**
+- Understanding model representations of code safety
+- Probing vulnerability direction geometry
+- Analyzing activation patterns across CWE types
+
+**Code generation safety:**
+- Evaluating whether language models prefer secure or vulnerable patterns
+- Testing security alignment in code assistants
+- Debugging security misalignment in LLM-generated code
+
+## Limitations & Known Issues
+
+1. **Language scope**: C code only. Multi-language analysis requires separate datasets (SVEN, PreciseBugs).
+2. **Real-world bias**: Reflects OSV/NVD coverage—some vulnerability types may be overrepresented.
+3. **Patch simplicity**: Many fixes are removals/simplifications rather than structural additions. Mean vulnerable code is 20% longer.
+4. **CWE distribution**: Some CWE categories have <30 pairs; use caution with small-sample types.
+
+## Replication & Validation
+
+The core findings have been validated on two additional C vulnerability datasets:
+
+- **SVEN** (Semantic Vulnerability Extraction Network): 423 paired samples across 6 CWEs, synthetically curated
+- **PreciseBugs**: 4,101 paired samples from open-source repositories across 9 CWEs
+
+Results consistently replicate across all three datasets.
+
+## Related Work
+
+- **CVSS**: Common Vulnerability Scoring System (severity ratings)
+- **CWSS**: Common Weakness Scoring System (weakness prioritization)
+- **SonarQube**: Static analysis for vulnerability detection
+- **Semgrep**: Lightweight vulnerability scanning
+
+## Contact & Issues
+
+For dataset issues, questions, or contributions:
+- GitHub: https://github.com/rufimelo/DeltaSecommits/edit/main/README.md)
+- Email: rmelo@cs.cmu.edu
+
+## License
+
+DeltaSecommits is provided under the **MIT License**. Code samples are derived from publicly disclosed vulnerabilities in the OSV and NVD databases and are used in accordance with those databases' terms.
 
 ## 📚 Citation