Aikido Machine (Product): news timeline & CVEs

Aikido Security Releases Altar-1: An Open-Weight Security Model Pruned From GLM-5.3 to 328 GB

Aikido released Altar-1, an open-weight security model pruned from GLM-5.3 to 328 GB.

Aikido Security published Altar-1, a compressed derivative of Z.AI’s 753-billion-parameter GLM-5.3 mixture-of-experts model, for on-prem and air-gapped security use. It starts from an AWQ INT4 checkpoint and uses Cerebras REAP to remove 88 of 256 routed experts per layer, cutting weights from 1,506.7 GB in BF16 to 328 GB while keeping about 40 billion active parameters. On Aikido’s 32-vulnerability internal benchmark, average recall fell from 65.6% to 60.4%, still covering 23 of 32 cases. Weights are on Hugging Face and run in vLLM on four NVIDIA H200 GPUs under the GLM-5.3 license.

MarkTechPostSep 25, 15:14 UTC · 1d agoModel release 2 sources

Aikido Security Unveils Altar-1 Open-Weight AI for Cybersecurity Defense

Aikido released Altar-1, an open-weight defensive model pruned from GLM-5.3 for on-premises security testing.

Aikido Security released Altar-1, an open-weight model for vulnerability discovery and penetration testing that runs inside customer infrastructure. It is derived from Z.AI's GLM-5.3 and compressed from 1.51 TB to 328 GB with AWQ INT4 quantization and Cerebras REAP pruning that retained 168 of 256 routed experts. On 32 known vulnerabilities across 30 repositories, Altar-1 averaged 60.4 percent recall and rediscovered 23 flaws, compared with 65.6 percent recall and 25 flaws for the full-precision parent. It is on Hugging Face and can run with vLLM on four NVIDIA H200 GPUs, including in the Aikido Machine appliance.

Cyber Security Newsupdated Sep 25, 15:14 UTC · 1d agofirst Sep 22, 10:50 UTC · 4d agoModel release 2 sources

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Aikido Security Releases Altar-1: An Open-Weight Security Model Pruned From GLM-5.3 to 328 GB

Aikido Security Unveils Altar-1 Open-Weight AI for Cybersecurity Defense