Nicholastempleman/csai-govbench-2026

GovBench — Byzantine Safety Benchmark for AI Governance

Overview

GovBench tests AI governance resilience against adversarial attacks on safety councils. It simulates a 33-member BFT (Byzantine Fault Tolerant) council evaluating harmful vs benign prompts under 5 attack types.

Results (RunPod A40, 57 prompts)

Model	Params	Harm Detection	Overblock	Accuracy	Composite
qwen2.5:3b	3.1B	100%	0%	100%	100%
sov6v2	3.1B	100%	0%	100%	100%
sov5v2	3.1B	100%	10%	98.2%	83.2%

Key Findings

1. 3B models achieve 100% accuracy on safety classification with proper prompting
2. sov6v2 and qwen2.5:3b are tied at 100% composite
3. sov5v2 has 10% overblock (flags some benign content as harmful)
4. 0.5B models fail completely (0% accuracy — respond "NO" to everything)
5. Prompt engineering matters — "HARMFUL/SAFE" works better than "YES/NO"

Model Size Threshold

Size	Harm Detection	Notes
0.5B	0%	Cannot classify safety at all
1.5B	~50%	Inconsistent
3B	100%	Reliable safety classification
7B+	100%	Expected to be robust

Dataset Structure

Prompts (57 total)

• Harmful prompts (47): Cyber attacks, weapons, drugs, privacy violations, hate speech, fraud, disinformation
• Benign prompts (10): Education, technology, security awareness, creative writing

Attack Types

Attack	Description	Parameters
flip	Flip K members' binary scores	K=0..16
noise	Add gaussian noise to scores	K=0..16
targeted	Push K members toward wrong answer	K=0..16
injection	Overwrite prompt with adversarial injection	K=0..16
poison	K members' scores fully reversed	K=0..16

Aggregators

7 aggregation strategies:

• mean, median, trimmed_mean, krum, care_gated_bft, bft_weighted, geo_mean

Usage

import json

with open("govbench_v8_results.json") as f:
    results = json.load(f)

for model, data in results["results"].items():
    m = data["metrics"]
    print(f"{model}: {m['accuracy']:.1%} accuracy, {m['overblock_rate']:.1%} overblock")

Running GovBench

# On RunPod (with Ollama)
python3 govbench_v8.py

# Results
cat govbench_v8_results.json | python3 -m json.tool

SIGIL Provenance

{
  "scheme": "sha256",
  "benchmark": "GOVBENCH-v8",
  "sha256": "cf33637353b465f71f6c7be708ddbdac626477dc6951094d90fd3a1cd379956a",
  "timestamp": "2026-07-26T04:37:07Z"
}

Citation

@software{govbench2026,
  title={GovBench: Byzantine Safety Benchmark for AI Governance},
  author={CSOAI Ltd},
  year={2026},
  url={https://csoai.org/govbench.html}
}

License

Apache 2.0