Nullsec-S1

Nullsec-S1 is an open-source security model purpose-built to audit AI-generated apps, agents, and vibecoded software before they reach production.

This repository contains the RC2/v1.1 PEFT / QLoRA adapter for Qwen/Qwen2.5-Coder-7B-Instruct. It is an adapter release, not merged full model weights. Users need the base model plus this adapter.

Release

• Model name: Nullsec-S1
• Release: RC2/v1.1
• GitHub release tag: v1.0.0-rc25
• Release artifact commit: c29c7f1
• Base model: Qwen/Qwen2.5-Coder-7B-Instruct
• Adapter type: PEFT / QLoRA
• Adapter weights: adapter_model.safetensors
• Tokenizer/chat template: included with this adapter repository

What it is

Nullsec-S1 returns final structured JSON security audit verdicts for application code, AI-generated apps, autonomous agents, MCP tools, Web3/wallet flows, and common application-security failures.

S1 means Security-1. Nullsec-S1 does not expose a hidden reasoning-token loop, <thought> format, or chain-of-thought parser. It emits a final structured security audit.

Intended use

• Auditing AI-generated applications before deployment
• Reviewing autonomous-agent and MCP tool risk
• Reviewing Web3/wallet approval and transaction flows
• Generating structured security verdicts for CI, API, or CLI integrations
• Producing secure patch guidance for detected findings

Out of scope

• Not a general chatbot
• Not trained from scratch
• Not a replacement for human security review
• Not a guarantee of zero vulnerabilities
• Not a universal production-safety guarantee
• No "first", "only", or "best" claim is made

How to load with Transformers + PEFT

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

base_model = "Qwen/Qwen2.5-Coder-7B-Instruct"
adapter_id = "trynullsec/nullsec-s1"

quant = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(
    base_model,
    quantization_config=quant,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, adapter_id)
model.eval()

Prompt format

Use the tokenizer chat template. The recommended user message is:

Audit the following code for security vulnerabilities. Emit only the JSON verdict.

FILE: app/api/admin/route.ts
```typescript
<code here>
```

Use a system instruction equivalent to:

You are Nullsec-1, a strict security review model. You are NOT a chatbot and you do not write features. Your only job is to audit code for security risk and emit a single JSON verdict.

Output schema

Nullsec-S1 is trained to emit a single JSON object with:

• risk_score
• production_ready
• severity
• confidence
• reasoning_summary
• exploit_scenario
• affected_files
• checks_performed
• findings

Safe code should return an empty findings array:

{
  "risk_score": 0,
  "production_ready": true,
  "severity": "INFO",
  "findings": []
}

Unsafe code should include one finding per independent issue. Downstream systems should still run deterministic schema alignment and safety enforcement over the raw model output.

Evaluation results

On the Nullsec RC2/v1.1 111-case security benchmark:

Metric	Result
raw outputs	111/111
detection F1	0.9245
precision	0.9423
recall	0.9074
false_safe_rate	0.0
safety probes	passed

These results are benchmark-scoped and tied to the v1.0.0-rc25 release artifacts.

Baseline comparison

On the same Nullsec RC2/v1.1 benchmark:

System / tool	F1
Nullsec-S1 RC2/v1.1	0.9245
OpenAI/Codex gpt-5.3-codex	0.7252
Claude Opus 4.8	0.6550
Semgrep local rules	0.5535
Qwen2.5-Coder-7B-Instruct base	0.0180

Baseline results are produced by project scripts and should be reproduced from the repository for comparison. They are not universal claims about any provider or tool.

Limitations

• The benchmark is repo-authored and security-specific.
• Benchmark performance does not guarantee every vulnerability will be detected in arbitrary real-world code.
• Independent security review is recommended for critical systems.
• Patch correctness is structurally measured; compile/run/test verification is future work.
• Hosted-provider baselines can change over time as provider models change.
• This adapter is not merged full weights; users must load the base model.

Safety and non-claims

Nullsec-S1's production_ready field is advisory until deterministic safety enforcement is applied. In the Nullsec repository, the Security Alignment Layer and Safety Layer recompute and enforce production readiness.

This release does not claim:

• first, only, or best model status
• guaranteed secure code
• zero vulnerabilities
• replacement for human security review
• universal production safety

Provenance

• GitHub repo: https://github.com/trynullsec/nullsec-s1
• GitHub release: https://github.com/trynullsec/nullsec-s1/releases/tag/v1.0.0-rc25
• Base model: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct