3DCF-Labs-org

ProximaA-1.0

README

License: apache-2.0

What it does

Detect vulnerabilities. Given code, it flags security issues and names the likely class.
Validate false positives. It tells a confirmed issue from scanner noise, and says what evidence is needed to be sure.
Verify patches. It checks that a fix addresses the real cause and suggests regression tests.
Write secure fixes. Given a report or code, it finds the root cause and produces a safe, minimal fix with tests.
Reason and plan. It works through audits, detection rules, and remediation steps.

It is trained across web and API security, smart contracts (EVM and non-EVM), supply chain and CI/CD, cloud and infrastructure, and LLM/AI application security. It is a defensive tool and does not help with attacks — see ACCEPTABLE_USE.

Files

Table with columns: File, Use it for
File	Use it for
full-precision weights	GPU serving (e.g. vLLM)
`proximaa-1.0-Q5_K_M.gguf`	CPU/GPU serving, higher quality
`proximaa-1.0-Q4_K_M.gguf`	CPU/GPU serving, smaller and faster (default)

Quickstart

Ollama

bash
ollama run hf.co/3DCF-Labs-org/ProximaA-1.0-GGUF:Q4_K_M

llama.cpp

bash
llama-server -m proximaa-1.0-Q4_K_M.gguf -c 4096

vLLM

bash
vllm serve 3DCF-Labs-org/ProximaA-1.0 --dtype bfloat16 --max-model-len 4096

transformers

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "3DCF-Labs-org/ProximaA-1.0"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

messages = [
    {"role": "system", "content": "You are a defensive secure-code assistant."},
    {"role": "user", "content": "Review this code for security issues and name the class:\n\n<your code>"},
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=1024, temperature=0.0, do_sample=False)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

Modes

ProximaA has six built-in security modes — detect, triage, validate, fix, verify, plan — each with a ready-made system prompt and user template. The prompts and a small client are in the GitHub repo.

How good it is

Trained specifically on cybersecurity data, ProximaA 1.0 outperforms the open base models it builds on on defensive-security work while keeping strong general reasoning and coding. Measured on standard public benchmarks and an internal security test set.

Table with columns: Area, Benchmark, Score
Area	Benchmark	Score
Vulnerability detection (in-domain)	identify + classify held-out issues	0.97
Secure fixes	internal security fix benchmark	leads its base models
Coding	HumanEval (pass@1)	0.63
Math reasoning	GSM8K	0.89
Knowledge	MMLU	0.78
Reasoning	ARC-Challenge

Notes:

Detection: on held-out security material in its trained domains, it correctly identifies and classifies vulnerabilities 97% of the time. Generic C/C++ function-classification benchmarks are a known-hard task where no current model does much better than chance; those are not the model's target.
Safety: it refuses all requests for operational attack help (MITRE 0.00) and rarely refuses legitimate security work (2.5% over-refusal).
Reasoning: general reasoning is on par with comparable 32B models — the security focus does not weaken it.

ProximaA is a security specialist. It is not meant to compete with much larger general-purpose models on general tasks; it is meant to be the stronger choice for security work at its size.

Limitations

Always have a person review a fix before applying it. The model is an assistant, not a replacement for security review.
Resistance to prompt-injection attacks is moderate. Do not feed it untrusted input in a setting where that matters without your own guardrails.
Use it only for authorized, defensive work.

License and attribution

ProximaA 1.0 is released under Apache-2.0. It is built using open-source components released under Apache-2.0 and MIT. Full attribution is in NOTICE.

Citation

markdown
ProximaA 1.0
Yevhenii Molchanov
https://github.com/3DCF-Labs
2026

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

3DCF-Labs-org

Model Tree

Base

Qwen/Qwen2.5-Coder-32B-Instruct

Fine-tuned

this model

Input Modalities

Text

Output Modalities