Two models.
A shared discipline.
Choose CyberKimi or CyberGLM for specialized cybersecurity reasoning, from security context to actionable analysis.
CONCEPTUAL WORKFLOW · NOT A LIVE SESSION
Enter platform ↗
Two models, fine-tuned for cybersecurity.
Understand the adversary. Build a stronger defense.

Frontier-scale reasoning.
Focused on the work that matters.
CyberKimi.
Deep context.
Operational clarity.
CyberKimi and CyberGLM are both fine-tuned for cybersecurity. Two specialized models for the code, the evidence, and the decisions that define security work.
Choose CyberKimi or CyberGLM for specialized cybersecurity reasoning, from security context to actionable analysis.
CONCEPTUAL WORKFLOW · NOT A LIVE SESSION
Model ablation and cyber tuning, informed by investigations, research, and operational playbooks.
No GPU request logs. No training on customer prompts. Sensitive analysis belongs to your team.
From domain tuning toward reinforcement learning in verifiable security environments.
DEFENSIVE INTELLIGENCE
Connect the evidence. Understand the behavior. Give your team a clearer path to response.
Translate suspicious behaviors into Sigma, YARA, and KQL.
Reconstruct the sequence. Plan containment and recovery.
Turn threat intelligence into testable hypotheses.
Review the authentication events from this incident. Identify the sequence of suspicious activity, explain the evidence, and propose a focused detection strategy.
DEFINE THE OUTPUT78 / 90 tasks solved
65.6% solved on the first attempt.
Single-seed CyberKimi runs. Assisted and unassisted results are shown separately. These task-specific results are not a universal model ranking.
Source: CyberKimi public benchmark repository ↗ · Figures as published September 2026.
Bring specialized cybersecurity reasoning to your tools with CyberKimi or CyberGLM. See the documentation for model IDs and integration details.
# Connect to specialized cyber reasoning.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.adverserial.ai/v1",
api_key=os.environ["CYBERKIMI_API_KEY"]
)
result = client.chat.completions.create(
model="lordx64/cyberkimi",
messages=[{
"role": "user",
"content": "Help plan a threat hunt."
}]
)
print(result.choices[0].message.content)Prepaid access. No recurring membership.
Pay for the tokens you use.
Fine-tuned for cybersecurity.
Fine-tuned for cybersecurity.
Talk to us about your organization’s cybersecurity AI needs.
Specialized reasoning.
Built for the people who defend.
