AI NEWSLatest AI NewsArticleAnt Group Releases Two Open-Source Security Models, Putting a Tight Rein on Large Models and Intelligent AgentsPublished in Latest AI NewsTime :Jul 13, 2026Read :12minuteOn July 13, the Ant AI Security Lab announced the open-source of the intelligent agent safety barrier SingGuard-NSFA and disclosed detailed information about the multimodal safety barrier SingGuard. The two models are designed for two cutting-edge scenarios: “autonomous executing agents” and “multimodal interaction large models,” marking Ant’s further improvement of its systematic layout in the field of AI security.As AI moves from content generation to autonomous execution, security issues are expanding from model output to behavior control, permission management, and system governance. Intelligent agents now independently call tools, run code, and plan tasks, while models have gradually gained the ability to understand multimodal information such as images and text. As AI system capabilities continue to expand, new security challenges arise.In the past year, security incidents such as prompt injection, misuse of permissions, malicious code execution, and data leaks have continued to occur. Examples include the prompt poisoning of Amazon Q, the data leak of Microsoft Copilot, and the prompt injection risks exposed by the open-source agent OpenClaw. These cases show that the stronger the autonomy of intelligent agents, the more pronounced the amplification effect of security risks. In December 2025, OWASP released the “Top 10 Risks for Intelligent Agent Application Security,” systematically summarizing unique security threats of intelligent agents; in May 2026, the Cyberspace Administration of China, the National Development and Reform Commission, and the Ministry of Industry and Information Technology jointly issued the “Opinions on the Standardized Application and Innovative Development of Intelligent Agents,” which clearly outlined requirements for intelligent agent security governance at the national level for the first time.In this context, the open-sourcing of these two security models by the Ant AI Security Lab aims to provide more comprehensive underlying security capabilities for large models and intelligent agents.SingGuard-NSFA: Install a “Real-Time Brake” for Intelligent Agent OperationsAs intelligent agents move from “answering questions” to “performing tasks autonomously,” AI begins to call tools, execute code, and orchestrate multi-step tasks. Security risks are no longer limited to the content level but increasingly appear in the behavioral level. Issues such as prompt injection, sensitive information theft, malicious code execution, resource exhaustion, and permission abuse are difficult to effectively cover with traditional content review systems.To address this pain point, the Ant AI Security Lab introduced SingGuard-NSFA, which performs real-time security detection before an intelligent agent executes actions, building a behavioral security protection system from both request interception and response backup.SingGuard-NSFA is based on the CIA (Confidentiality, Integrity, Availability) principles and combines international security guidelines such as OWASP. It categorizes intelligent agent risks into seven major categories, 28 intermediate categories, and 185 specific scenarios, and establishes a comprehensive intelligent agent security evaluation system covering 133 languages and nearly 100,000 samples.In terms of technical implementation, SingGuard-NSFA meets both security audit and real-time protection needs. It provides two working modes: one mode can generate detailed risk analysis reports line by line, facilitating post-event review and compliance records; another mode can complete single-risk assessment within about 50 milliseconds, suitable for real-time interception in high-concurrency online scenarios. It also offers four model sizes: 0.8B, 2B, 4B, and 9B, meeting different deployment needs.Multiple public evaluations show that SingGuard-NSFA achieves leading performance in intelligent agent input and output security detection. For instance, the 0.8B model can achieve the performance level of an 8B model. When adding new risk categories, only a lightweight module needs training without retraining the entire model, further enhancing the detection capability of existing security barriers.SingGuard: A “Versatile Gatekeeper” for Multimodal Content SecurityAnother critical defense line parallel to intelligent agent operational security is content security in multimodal interaction scenarios. In June this year, Anthropic released its flagship model Claude Fable5, which was bypassed by researchers using Unicode characters and Cyrillic letters to replace sensitive words within days— the model could restore the original meaning, but the classifier regarded it as unfamiliar spelling, thus exposing the system prompt. This indicates that the more capable a model is at understanding distorted text