---
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- **Total Corpus Documents:** `1,788` (`447` merged active specifications, `447` superseded baseline specifications, `447` unmerged RFC/draft proposals, `447` multi-family runtime secret configurations)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}}
}
```