GPT-5.5-Cyber vs. Altar-1

GPT-5.5-Cyber is an advanced AI model designed for authorized cybersecurity professionals who need stronger support for vulnerability research, codebase analysis, and remediation. The model builds on GPT-5.5’s general-purpose intelligence while adding more capable and permissive behavior for specialized defensive security workflows. It is designed to help reduce unnecessary refusals for verified defenders while still pairing advanced capabilities with verification, monitoring, scoped controls, and review. GPT-5.5-Cyber can sustain deeper analysis across large and complex codebases, making it useful for identifying security-relevant components and tracing how vulnerabilities may be reached. It can also help validate likely issues in controlled environments, develop and test patches, and organize evidence for human security teams. The model is intended to support the full remediation loop, helping defenders move from discovery to validation to fix preparation instead of only producing raw vulnerability findings. In benchmark testing, GPT-5.5-Cyber outperformed GPT-5.5 on CyberGym, ExploitGym, and SEC-bench Pro. These results show improved performance in reproducing known vulnerabilities, reasoning through exploitability, and handling long-horizon vulnerability discovery and proof-of-concept workflows. The model is also being evaluated through complex repositories and real remediation workflows as coordinated disclosures conclude. GPT-5.5-Cyber is positioned as a higher-capability option for defenders whose authorized work requires the most advanced cyber support, while GPT-5.5 with Trusted Access for Cyber and Codex Security remains the recommended starting point for most defenders. GPT-5.5-Cyber helps qualified security teams work faster, validate vulnerabilities more effectively, and support safer remediation across critical software systems.

The Aikido Altar is a state-of-the-art open-weight security framework designed to provide organizations with sophisticated defensive security intelligence specifically adapted to their infrastructure needs. This system is particularly effective in environments that demand sovereign security, safeguarding sensitive materials such as source code, architectural blueprints, vulnerability reports, and various confidential data within the organization's perimeter, preventing any sharing with outside inference services. Utilizing the GLM-5.3 architecture, Altar incorporates methods like quantization and expert pruning that reduce the model size from a substantial 1.51 TB in full precision to a more manageable 328 GB, while still preserving most of the original model's reasoning and security capabilities. The architecture maintains 168 of the originally 256 routed experts in each backbone expert layer and employs a W4A16 representation, improving its utility for security tasks that necessitate managing large and evolving context windows. Expert selection was calibrated using internal pentesting data alongside multilingual resources, ensuring that no client data was involved, thus maintaining the integrity of cybersecurity, programming, and language processing capabilities. This groundbreaking strategy not only simplifies the deployment process but also enhances the organization's defense against new and evolving threats, ensuring robust protection in an increasingly complex digital landscape. Furthermore, the ongoing development of additional features aims to further bolster the system’s resilience and adaptability to future security challenges.