Can an Open Model Do Security Research? Cantina’s apex-flash-1 Solves 40 of 60 Held-Out Bug Tasks

Cantina Security, with Yeta Labs, has released apex-flash-1, an open-weights model trained specifically for vulnerability research. It is a reinforcement learning fine-tune of Z.ai’s GLM-5.3-Flash, released on Hugging Face under the MIT license.
Is it deployable? Yes, the MIT weights serve on vLLM, SGLang or Transformers, but BF16 needs roughly 640 GB of GPU memory.
What Cantina Built
apex-flash-1 has 321.3B total parameters, per its Hugging Face safetensors metadata. The GLM-5.3-Flash base is a Mixture-of-Experts model with 18B active parameters.
Cantina trained it with GRPO using a rank-256 LoRA plus selective full-parameter training. The data covers 150 tasks built from 50 real vulnerability cases.
Each case appears in 3 variants: guided whitebox, focused whitebox and focused blackbox.
Authorization, identity and scope flaws make up 72% of cases. Accounting and numerical precision bugs add 18%. Time validation, business rules and SSRF cover the rest.
As per the model card on HF, RL rollouts ran inside the Codex agent harness on production-like software and protocol environments.
Benchmark Results
Cantina evaluated 60 tasks from 20 held-out vulnerability cases. Each model ran the set once, with costs estimated from provider pricing.

apex-flash-1: 40/60 solved (66.7% pass@1), about $2.38
GLM-5.3-Flash (base): 36/60 solved (60.0%), about $4.56
Claude Opus 5 High: 43/60 solved (71.7%), about $74.68

Opus solved 3 more tasks but cost about 31x more per run. That is roughly $0.06 per solved task for apex-flash-1 versus $1.74 for Opus. These are company-reported numbers on an internal benchmark.
A Worker Model, Not an Orchestrator
Cantina positions apex-flash-1 as a worker orchestrated by a larger model. The card lists code reading, tool use, exploit development and verification as target skills.
An experimental apex-flash-1-abliterated variant ships with modified refusal behavior. It was not separately evaluated.
Cantina’s rationale is that defenders need capable models they can run and control locally.
Interactive Explainer

How It Compares

Feature
apex-flash-1
Aikido Altar-1
Cisco Foundation-Sec-8B-Reasoning
GLM-5.3-Flash

Developer
Cantina Security + Yeta Labs
Aikido Security
Cisco Foundation AI
Z.ai

Base model
GLM-5.3-Flash
GLM-5.3 (pruned)
Llama 3.1 8B
Own pretraining

Size
321.3B total, BF16
328 GB, INT4 (W4A16)
8B
320B total, 18B active

License
MIT
Inherits GLM-5.3 license
Custom (see NOTICE.md)
MIT

Security method
GRPO RL on 50 real vulnerability cases
Expert pruning (REAP) + quantization
Instruction tuning + RLHF on security QA
General-purpose base

Primary use
Agentic vuln research worker
Air-gapped autonomous pentesting
SOC triage and threat defense
General coding and agents

Hardware
Multi-GPU node (~642 GB BF16 weights)
4x H200 with vLLM
Single GPU
Multi-GPU node

Published security result
66.7% pass@1, 60 tasks
60.4% recall, 32-CVE internal set
Cisco-reported security benchmarks
60.0% on Cantina’s set

Sources: Cantina, Aikido, Cisco, Hugging Face model cards. © Marktechpost
Key Takeaways

apex-flash-1 is a 321.3B open-weights security model under MIT.
GRPO training on 50 real vulnerability cases produced 150 tasks.
It scored 66.7% pass@1 versus 71.7% for Claude Opus 5 High.
Its 60-task run cost about $2.38 versus $74.68 for Opus.
BF16 needs a multi-GPU node; community 4-bit ports exist.

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