| --- |
| license: apache-2.0 |
| task_categories: |
| - question-answering |
| - information-retrieval |
| language: |
| - en |
| tags: |
| - code |
| - github |
| - git |
| - benchmark |
| - coding-agents |
| - temporal-reasoning |
| - rag |
| - security |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: corpus |
| path: corpus.jsonl |
| - split: queries |
| path: queries.jsonl |
| - split: qrels |
| path: qrels.jsonl |
| --- |
| |
| # GitBench (v2): Real-World GitHub Pull Request State & Security Benchmark for AI Coding Agents |
|
|
| **GitBench** (originally *GitState-Bench*) is an open evaluation benchmark developed by the **Knot AI** team ([@Knot-Repo](https://github.com/Knot-Repo)) to rigorously evaluate how AI coding assistants, code search systems, and Retrieval-Augmented Generation (RAG) engines handle **temporal code evolution, obsolete baseline specifications, unmerged RFC proposals, and 12 families of production credentials**. |
|
|
| --- |
|
|
| ## Benchmark Motivation |
|
|
| Most coding benchmarks (e.g., HumanEval, MBPP, SWE-bench) evaluate whether a model can synthesize code from a static snapshot. However, in production engineering environments, developer context evolves continuously through Git commits and Pull Requests. |
|
|
| AI agents and vector retrieval layers regularly fail in three high-severity ways: |
| 1. **Temporal Obsolescence (The "Verbose Legacy vs. Concise Update" Trap):** Retrieving a verbose legacy architecture specification (`superseded_v1`) instead of a concise newly merged PR (`active_v2_gold`) because both share $>85\%$ of their domain vocabulary. |
| 2. **Brainstorm / Draft RFC Contamination:** Treating unmerged draft RFCs or exploratory proposals (`brainstorm_unconfirmed`) as authoritative production behavior. |
| 3. **High-Entropy Secret Leakage vs. False-Positive Over-Quarantine:** Leaking unprefixed HMAC keys or authenticated database/Redis DSN URIs into prompts (`admin_secret_trap`)—or conversely, falsely quarantining benign engineering notes that contain high-entropy Git commit SHAs and UUIDv4 trace IDs. |
|
|
| --- |
|
|
| ## Dataset Scale & Repository Coverage |
|
|
| - **Total Corpus Documents:** `1,788` (`447` merged active specifications, `447` superseded baseline specifications, `447` unmerged RFC/draft proposals, `447` multi-family runtime secret configurations) |
| - **Developer Queries:** `447` natural active implementation queries + `447` paired historical audit queries (`historical_text`) |
| - **Relevance Judgments (`qrels`):** `1,788` graded relevance pairs (`4,023` total records across splits) |
| - **25 Sampled Repositories:** |
| `fastapi/fastapi`, `pydantic/pydantic`, `anthropics/anthropic-sdk-python`, `vercel/ai`, `encode/httpx`, `psf/requests`, `pallets/flask`, `tiangolo/sqlmodel`, `openai/openai-python`, `langchain-ai/langchain`, `run-llama/llama_index`, `astral-sh/uv`, `huggingface/transformers`, `vllm-project/vllm`, `pandas-dev/pandas`, `numpy/numpy`, `scikit-learn/scikit-learn`, `pytorch/pytorch`, `sqlalchemy/sqlalchemy`, `celery/celery`, `pytest-dev/pytest`, `Textualize/rich`, `streamlit/streamlit`, `gradio-app/gradio`, `BerriAI/litellm`. |
|
|
| --- |
|
|
| ## 12-Family Credential & Benign High-Entropy Design |
|
|
| Unlike synthetic benchmarks that test only a single hardcoded token prefix (such as `ghp_`), GitBench v2 distributes `447` secret traps across **12 realistic credential families** while embedding benign high-entropy **Git commit SHAs** and **UUIDv4 trace identifiers** into normal engineering documents: |
| - **50% Unprefixed High-Entropy Secrets (6 Families):** `WEBHOOK_SIGNING_SECRET`, `JWT_HS256_PRIVATE_KEY`, `MASTER_ENCRYPTION_KEY`, `OAUTH_CLIENT_SECRET`, `INTERNAL_AUTH_TOKEN`, `SESSION_HMAC_SECRET` ($H \ge 3.85\text{ bits/char}$, no vendor prefix—missed completely by prefix regexes). |
| - **25% Authenticated Service & Database DSN URIs (3 Families):** PostgreSQL (`postgresql://...`), Redis Cluster (`redis://:...`), and AMQP/Celery (`amqp://...`) connection strings with high-entropy embedded passwords ($H \ge 3.10\text{ bits/char}$). |
| - **25% Vendor-Prefixed Tokens & Private Key Headers (3 Families):** AWS (`AKIA...`), Anthropic (`sk-ant-...`), and `OPENSSH PRIVATE KEY` blocks. |
|
|
| --- |
|
|
| ## Empirical Baseline Results (`all-MiniLM-L6-v2`, 384D) |
|
|
| | Architecture | Scoped Active `v2` (Invariant) | Global Multi-PR Top-1 | Historical Audit Recall@1 | Credential Leak @ 5 | Benign Code False Quarantines | In-Memory Latency | |
| | :--- | :---: | :---: | :---: | :---: | :---: | :---: | |
| | **Standard Vector RAG (HNSW Cosine)** | `60.2%` | `56.8%` | `43.0%` | `65.1%` | `0 / 1,341` | `0.013 ms` | |
| | **Hybrid Lexical + Vendor-Prefix Regex Only** | `63.5%` | `61.5%` | `43.0%` | `49.7%` (Misses unprefixed) | `0 / 1,341` | `0.070 ms` | |
| | **Hybrid Lexical + Unconditioned Shannon Entropy** | `7.6%` | `8.5%` | `43.0%` | `1.3%` | `1,020 / 1,341` (SHAs/UUIDs) | `0.068 ms` | |
| | **Knot CARMA-16 Engine + UCS Protocol** | **`100.0%`** (0 Stale/Draft) | **`89.5%`** | **`86.4%`** | **`0.0%`** (12/12 Families) | **`0 / 1,341`** (`0.0%` FPR) | **`0.079 ms`** | |
|
|
| --- |
|
|
| ## Quickstart |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load GitBench v2 directly from Hugging Face (4,023 records) |
| corpus = load_dataset("knot-ai/gitbench", split="corpus") # 1,788 PR state documents |
| queries = load_dataset("knot-ai/gitbench", split="queries") # 447 active + historical queries |
| qrels = load_dataset("knot-ai/gitbench", split="qrels") # 1,788 TREC/BEIR relevance judgments |
| ``` |
|
|
| --- |
|
|
| ## Citation & Attribution |
|
|
| ```bibtex |
| @misc{knot_ai_gitbench_2026, |
| author = {Knot AI Team}, |
| title = {GitBench: Evaluating Temporal Causal Freshness and Secret Containment in AI Coding Agents}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| howpublished = {\url{https://huggingface.co/datasets/knot-ai/gitbench}} |
| } |
| ``` |
|
|
