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Cisco has launched Antares-350M and Antares-1B, two cyber-focused small language models built to identify vulnerable files in source-code repositories from vulnerability descriptions. The company said the open-weight models will be released on Hugging Face and are designed to run locally, allowing security teams to analyze sensitive codebases without sending source code to cloud-hosted frontier models. Cisco positioned Antares for repository-level vulnerability localization, advisory triage, static-analysis augmentation, and shift-left security workflows in CI/CD pipelines.
Cisco said Antares mimics a human investigator by tracing relevant code patterns, reviewing candidate files, and narrowing findings as new evidence emerges. In its announcement and related reporting, Cisco claimed the models can perform repository analysis at a fraction of the compute cost of larger general-purpose models, citing a 500-entry evaluation completed in 15 minutes on a single GPU for less than $1. Cisco also said Antares outperformed several OpenAI and Google models on vulnerability-finding benchmarks, though reporting noted it did not exceed OpenAI's premium GPT-5.5 (xhigh) and that Anthropic's Claude Opus 4.6 scored higher in a Cisco technical report.
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Cisco said the Antares family also includes a 3B parameter model and described support for local inference, repository-wide scans, targeted CWE investigations, and SARIF output. The company positioned these features as enabling low-cost deployment while keeping proprietary code within an organization's trust boundary.
Cisco Foundation AI released a 500-entry Vulnerability Localization Benchmark to evaluate repository-level vulnerability localization across unfamiliar codebases and CWE-linked vulnerability patterns. Cisco said the benchmark showed Antares performing faster and at lower cost than larger competing models while supporting human-led triage.
Cisco announced the Antares family of open-weight small language models for repository-level vulnerability localization, including Antares-350M and Antares-1B. The company positioned them as efficient models for codebase analysis and said they would be released on Hugging Face.
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Security researchers and AI vendors reported rapid progress in applying frontier models to offensive and defensive cybersecurity work, including the discovery of **`CVE-2025-37899`**, a remote zero-day in the Linux kernel’s SMB implementation. A published account described using an advanced model to help identify the flaw, while OpenAI's **Daybreak** and Anthropic's **Project Glasswing** highlighted broader efforts to build AI systems for cyber operations, vulnerability research, and security workflows. SentinelLABS added evidence that these systems are moving beyond isolated tasks into sustained investigations, describing an eight-stage benchmark based on reverse engineering **fast16**, a 2005 Windows sabotage toolkit tied to interference with high-precision solvers used in nuclear-weapons modeling. In that evaluation, **GPT-5.6 Sol** was reported as the only publicly available model tested that completed the full long-horizon malware-analysis workflow, while other models showed useful point-in-time reasoning but failed to maintain consistency as new evidence forced earlier conclusions to be revised.
Jul 22, 2026
Anthropic disclosed **Mythos Preview**, an advanced AI model it says can identify and exploit zero-day vulnerabilities at a far higher rate than its Claude Opus 4.6 model, generating working exploits in **72.4 percent** of attempts. The company said the system can find and chain flaws across major operating systems and web browsers, including **remote code execution**, **sandbox escapes**, **local privilege escalation**, and multi-bug exploit paths. Anthropic did not release the model publicly, instead restricting access through **Project Glasswing** for selected partners and organizations to support defensive vulnerability research and responsible disclosure; it said the model has already uncovered thousands of additional high- and critical-severity flaws. At **Black Hat Asia**, RunSybil CEO and former OpenAI security engineer Ari Herbert-Voss said open source AI models can match Mythos-level bug-finding performance when paired with the right orchestration or "scaffolding." He said combining multiple open models can improve coverage because different systems surface different classes of flaws, offering a form of defense in depth, while also addressing the cost and limited availability of proprietary tools like Mythos. Herbert-Voss added that human experts remain necessary to coordinate model workflows and validate large volumes of AI-generated findings, but said economic pressure and operational advantages are likely to drive broader adoption of AI-assisted vulnerability discovery across security teams.
Jun 29, 2026
Anthropic released **Claude Opus 4.6**, highlighting improved *agentic* coding performance (code review, debugging, and sustained work over large codebases) and expanded safety evaluation coverage. The company claims the model is better at finding real vulnerabilities in codebases and behaves more consistently on complex tasks, while maintaining low rates of misaligned behavior (e.g., deception) and reducing unnecessary refusals to benign requests. Anthropic also reported using Claude Opus 4.6 to identify **500+ previously unknown high-severity flaws** in widely used open-source libraries, including **Ghostscript**, **OpenSC**, and **CGIF**, and said the issues were validated to avoid hallucinations and have been patched by maintainers. The company described testing by its **Frontier Red Team** in a virtualized environment with access to tools like debuggers and fuzzers, aiming to measure the model’s out-of-the-box vulnerability discovery capability without specialized prompting or custom scaffolding, and using the model to help prioritize severe memory-corruption findings.
Jun 29, 2026