GPT-5.5-Cyber
GPT-5.5-Cyber is an advanced cybersecurity-focused AI model designed for verified defenders working on authorized security research, vulnerability discovery, and remediation. The model pairs stronger cyber capabilities with more permissive behavior for specialized workflows that require deep analysis across complex software environments. It can help identify security-relevant components, trace vulnerable code paths, validate likely issues in controlled settings, develop and test patches, and prepare evidence for human review. GPT-5.5-Cyber is built to support the full remediation loop rather than simply generating more findings. The model shows stronger benchmark performance than GPT-5.5 on CyberGym, ExploitGym, and SEC-bench Pro, reflecting improvements in vulnerability reproduction, exploit reasoning, and long-horizon security tasks. GPT-5.5-Cyber is intended for advanced, authorized cybersecurity work with verification, monitoring, scoped controls, and review.
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ZeroPath
ZeroPath (YC S24) is an AI-native application security platform that delivers comprehensive code protection beyond traditional SAST. Founded by security engineers from Tesla and Google, ZeroPath combines large language models with advanced program analysis to find and automatically fix vulnerabilities.
ZeroPath provides complete security coverage:
1. AI-powered SAST for business logic flaws & broken authentication
2. SCA with reachability analysis
3. Secrets detection and validation
4. Infrastructure as Code
5. Automated patch generation.
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ZeroPath delivers 2x more real vulnerabilities with 75% fewer false positives.
Our research team has been successful in finding vulns like critical account takeover in better-auth (CVE-2025-61928, 300k+ weekly downloads), identifying 170+ verified bugs in curl, and discovering 0-days in production systems at Netflix, Hulu, and Salesforce.
Trusted by 750+ companies and performing 200k+ code scans monthly.
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GLM-5.3
GLM-5.3 is Z.ai’s frontier coding model designed for complex software engineering, long-horizon agent tasks, and advanced post-training research. The model uses the same base model as GLM-5.2, with improvements coming from scaled post-training across more environments, more diverse tasks, and larger compute investment. GLM-5.3 delivers stronger coding performance, better task ownership, improved benchmark results, and greater efficiency across realistic development workflows. It is built to handle complex coding tasks, production-style engineering work, research environments, automation tasks, and agentic workflows that require multi-step execution. The model also shows emergent cyber capabilities in vulnerability discovery and exploitation-chain reasoning, with safety evaluation and hardening planned before open-weight release.
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Fugu Cyber
Fugu Cyber is a specialized multi-agent orchestration model purpose-built for modern cyber defense. It behaves like a single model through one API endpoint, but dynamically coordinates specialized agents to solve complex, multi-step security tasks without depending on one model provider. It focuses on two core defense workflows, analyzing complex codebases to verify real-world vulnerabilities and translating raw cyber threat intelligence into working detection rules. On CyberGym, which evaluates vulnerability analysis and verification, Fugu Cyber achieved an 86.9% success rate; on CTI-REALM, which measures detection-rule generation from threat reports, it reached 72.1%, placing it alongside leading cyber-focused frontier models. Fugu Cyber is intended to work as the reasoning engine inside broader security systems rather than as a standalone solution.
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