{"id":16496,"date":"2026-07-22T16:51:57","date_gmt":"2026-07-22T16:51:57","guid":{"rendered":"https:\/\/newestek.com\/?p=16496"},"modified":"2026-07-22T16:51:57","modified_gmt":"2026-07-22T16:51:57","slug":"ciscos-new-ai-model-tells-code-reviewers-where-to-look-for-vulnerabilities","status":"publish","type":"post","link":"https:\/\/newestek.com\/?p=16496","title":{"rendered":"Cisco\u2019s new AI model tells code reviewers where to look for vulnerabilities"},"content":{"rendered":"
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Cisco has revealed a family of open-weight AI models called Antares that, it said, can help security teams isolate potentially vulnerable parts of a software repository before deeper investigation begins.<\/p>\n

Rather than detecting a specific CVE or generating a patch, these models search a codebase using only a Common Weakness Enumeration (CWE) description and return the files most likely to contain that class of vulnerability.<\/p>\n

\u201cIts purpose is to reduce a large codebase to a focused set of files that a security professional or a downstream security workflow should investigate,\u201d Cisco\u2019s AI researcher Supriti Vijay<\/a> said via email. \u201cThe goal is not to replace a security engineer\u2019s judgement or send them on a wild-goose chase, but to reduce fatigue and workload by helping them triage an issue earlier and focus their investigation on the most relevant parts of the codebase.\u201d<\/p>\n

The Antares family consists of models with 350 million, 1 billion, and 3 billion parameters trained specifically for repository-scale vulnerability localization.<\/p>\n

The company said its largest model approaches the performance of GPT-5.5 on its internal vulnerability localization (Vloc) benchmark while remaining small enough for low-cost local deployment.<\/p>\n

A search assistant, not a vulnerability detector<\/h2>\n

Cisco is careful to define what Antares is, and what it is not.<\/p>\n

\u201cAntares outputs a ranked list of source files likely to contain a relevant vulnerability, along with the terminal exploration trace that led to that result,\u201d Cisco Foundation AI Chief Scientist Amin Karbasi<\/a> wrote in a blog post, adding that the models are not meant to replace the broader application security toolchain: Human analysts or downstream security tools will still be needed to confirm exploitability, identify vulnerable lines of code<\/a>, assess severity and generate fixes.<\/p>\n

Antares differs from conventional static analysis platforms such as Semgrep or CodeQL, which primarily rely on predefined rules or queries. Cisco instead describes Antares as an evidence-driven exploration agent that adapts its search as it traverses the repository.<\/p>\n

Cisco\u2019s argument is that large repositories often contain thousands of files, making manual reviews exhaustive and unrealistic. By reducing the search space to a manageable shortlist, the company hopes to reduce investigation fatigue without replacing human judgement.<\/p>\n

Claims of specialization over scale<\/h2>\n

Cisco is also making a statement about how cybersecurity models should evolve.<\/p>\n

Instead of pursuing larger foundational models, Cisco argued that specialized, task-trained models can outperform much larger open-weight alternatives for vulnerability localization. In its evaluation Antares-3B, the largest model intended for single-GPU deployments, produced results comparable to GPT-5.5 while outperforming several substantially larger open models by Google, OpenAI and Meta.<\/p>\n

The family also includes Antares-350M for resource-constrained environments and Antares-1B for laptops and workstations, which Cisco has made available as open-weight models on Hugging Face.<\/p>\n

The command line interface (CLI) on the models supports targeted CWE investigations, repository-wide scans, SARIF output and local inference, which Cisco said enables organizations to keep proprietary code inside their own trust boundary.<\/p>\n

However, because Antares identifies candidate files rather than confirmed vulnerabilities, organizations will still need to understand how often such repository-wide searches should be run, how much they improve existing triage workflows, and whether the reduction in investigation effort ultimately translates into measurable security or cost benefits.<\/p>\n

This article first appeared on InfoWorld<\/a>.<\/em><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Cisco has revealed a family of open-weight AI models called Antares that, it said, can help security teams isolate potentially vulnerable parts of a software repository before deeper investigation begins. Rather than detecting a specific CVE or generating a patch, these models search a codebase using only a Common Weakness Enumeration (CWE) description and return the files most likely to contain that class of vulnerability…. <\/p>\n

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