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Trendyol/
Trendyol-Cybersecurity-LLM-v2-70B-Q4_K_M

Name: Trendyol-Cybersecurity-LLM-v2-70B-Q4_K_M
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Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset

AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.0

AlicanKiraz0/Cybersecurity-Dataset-Heimdall-v1.1

meta-llama/Llama-3.3-70B-Instruct

quantized

apache-2.0

endpoints_compatible

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Trendyol/Trendyol-Cybersecurity-LLM-v2-70B-Q4_K_M (Llama‑3.3‑70B Finetuned)

v2-Max is a defense-focused, alignment-safe cybersecurity language model based on Llama-3.3-70B. It was trained via SFT from scratch on the v2 dataset and ranked near the top on CS-Eval in the EN and EN-ZH tracks. Developed by the Trendyol Group Security Team.

Developed Trendyol Group Security Team

Alican Kiraz
İsmail Yavuz
Melih Yılmaz
Mertcan Kondur
Rıza Sabuncu
Özgün Kultekin

We thank Ahmet Gökhan Yalçın, Cenk Çivici, Nezir Alp, Yiğit Darçın for all their support.

🏆 CS‑Eval Benchmark Results

English: 3rd place
English–Chinese: 5th place
Comprehensive average: 91.03

Category	Score
Fundamentals of System Security and Software Security	91.33
Access Control and Identity Management	90.23
Encryption Technology and Key Management	92.70
Infrastructure Security	91.85
AI and Network Security	94.55
Vulnerability Management and Penetration Testing	90.78
Threat Detection and Prevention	92.44
Data Security and Privacy Protection	90.48
Supply Chain Security	93.69
Security Architecture Design	87.80
Business Continuity & Emergency Response / Recovery	85.00
Chinese Task	91.07

🔁 Why v2-Max?

Differences: Old model (Qwen3-14B, “BaronLLM v2.0”) → New model (Llama-3.3-70B, “v2-Max”):

Base model leap: 14B → 70B (stronger reasoning, long-range context, and standards-compliant answers).
Dataset: v1.1 (21,258) → v2.0 (83,920) rows (≈4×); coverage includes OWASP, MITRE ATT&CK, NIST CSF, CIS, ASD Essential 8, modern identity (OAuth2/OIDC/SAML), TLS, Cloud & DevSecOps, Cryptography, and AI Security.
Security gates: adversarial refusal tests against jailbreak/prompt injection, static policy linting, content risk taxonomy; refuse-or-report strategies in system prompts.
Orientation: offensive examples were removed; defensive, safe outputs are preferred.

📚 v2 Dataset Summary (Alignment-Safe SFT)

Size: 83,920 system/user/assistant triplets License: Apache-2.0 Language: English Split: train (100%) Format: Parquet (columns: system, user, assistant) Quality Gates: Deduplication, PII scrubbing, hallucination screening, adversarial refusal tests, static policy linting, risk taxonomy, strict schema validations (stable row IDs).

Coverage (key topics):

OWASP Top 10, MITRE ATT&CK, NIST CSF, CIS Controls, ASD Essential 8
Modern identity: OAuth 2.0 / OIDC / SAML
SSL/TLS practices and certificate hygiene
Cloud & DevSecOps: IAM, secrets management, CI/CD, container/K8s hardening, logging/SIEM, IR runbooks
Cryptography implementation hygiene
AI Security (prompt injection, data poisoning, model/embedding security, etc.)

The dataset is commercial-friendly: Apache-2.0. Harmful/exploitative content and high-risk materials such as raw shellcode were excluded during dataset construction; patterns for generating safe alternatives to harmful requests were included.

✨ Features

Defense-Focused Reasoning: Standards-aligned (OWASP/ATT&CK/NIST/CIS) recommendations with step-by-step explanations that include the “why/evidence.”
Policy & Architecture Guides: Identity/access, encryption, network segmentation, cloud control sets, data classification, and alert logic.
IR & Threat Hunting Support: Incident flow, triage checklists, safe log query patterns, playbook skeletons.
Cloud & DevSecOps: CI/CD security gates, IaC misconfiguration patterns, K8s hardening checklists.
Refusal-by-Design: Safe alternatives and compliance-aligned response patterns for exploitative/malicious prompts.

Note: This model is SFT-based; it has no tool use, web browsing, or access to an executable environment. In expert operations it is an assistant; it does not, by itself, constitute evidence.

🛠️ Quick Start

Transformers (HF)

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Trendyol/Trendyol-Cybersecurity-LLM-v2-70B-Q4_K_M"
tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

def generate(prompt, max_new_tokens=512, temperature=0.3, top_p=0.9):
    inputs = tok(prompt, return_tensors="pt").to(model.device)
    out = model.generate(**inputs, max_new_tokens=max_new_tokens,
                         do_sample=(temperature>0), temperature=temperature, top_p=top_p)
    return tok.decode(out[0], skip_special_tokens=True)

print(generate("Map a PCI DSS v4.0 compliance checklist for a multi-account AWS environment."))

vLLM

vllm serve Trendyol/Trendyol-Cybersecurity-LLM-v2-70B-Q4_K_M --dtype bfloat16

GGUF / llama.cpp

For practical single-machine use with a 70B model, Q4_K_M is recommended; higher-quality GGUF variants require much more memory.

🧪 Recommended Prompt Patterns

Goal	Template	Note
Risk Assessment	`ROLE: Security Architect\nCONTEXT: ...\nTASK: Produce a control-by-control gap analysis against NIST CSF v2.0...`	`temperature=0.2–0.4, top_p=0.9`
IR Playbook	`Create a phase-by-phase incident response playbook for ransomware in hybrid (Azure+on-prem) AD.`	Ask for a validation checklist in the final step.
Identity Security	`Propose hardened OAuth2/OIDC configs for a multi-tenant SPA+API.`	Request an "abuse cases" list at the end of the response.
Cloud Guardrails	`Generate K8s hardening controls mapped to CIS + NSA/CISA.`	Include “rationale” and “validation” columns.

⚖️ Intended Use, Limits, and Safety

Intended Use: Enterprise defense consulting, architectural guidance, control mappings, IR/TI support outputs, training, and documentation.

Prohibited / Limitations:

Unauthorized penetration testing, PoC exploit/payload generation, instructions that harm live systems.
Training or enriching outputs with personal data or confidential information.

Model Behavior:

Explicit refusal for harmful requests + safe alternative suggestions.
Content policies are tested within the dataset (against jailbreak/prompt injection).

Disclaimer: Model outputs should not be applied automatically without expert review; corporate policy and regulation take precedence.

🔍 Technical Notes

Training Method: SFT (instruction tuning) + system-prompt guardrails.
Context Window: Llama-3.3-70B’s default (same as Meta’s release).
Languages: Training language is English; EN-ZH evaluation is supported.
Release: Weights under the Meta Llama 3.3 License; dataset under Apache-2.0.

👥 Contributions & Contact

Issues / Feedback: HF or GitHub Issues
Research Collaboration: Trendyol Group Security Team
Acknowledgments: Trendyol Security Team, community contributors, and independent evaluators

🧾 Changelog

2025-10-06 — v2-Max: Migration to the Llama-3.3-70B base; v2 dataset with 83,920 rows; alignment security gates; CS-Eval EN #3 / EN-ZH #5, average score 91.03.