AI Coding Live News

Google releases Gemini 4 Carbon two weeks after Argon: 59 and first place on the Artificial Analysis Intelligence Index v5, state-of-the-art cybersecurity and long-horizon coding, with early access for cyber defenders in the Fairwind program

Google DeepMind announced Gemini 4 Carbon on Oct 28, calling it its strongest model, just two weeks after Gemini 4 Argon. The official blog says it ranks first on v5.0 of the Artificial Analysis Intelligence Index at 59 and leads three cybersecurity evaluations: 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation and 71.0% on CWE-bench for patching; it also gains 89 points to rank fourth on WebDev Arena at 1769. Access starts this week for a subset of cyber defenders in the Fairwind program, with developers, businesses and consumers to follow. Introductory pricing matches Argon at $2 per million input tokens and $10 per million output tokens, rising to $4 and $20 afterwards.

Google releases Gemini 4 Carbon two weeks after Argon: 59 and first place on the Artificial Analysis Intelligence Index v5, state-of-the-art cybersecurity and long-horizon coding, with early access for cyber defenders in the Fairwind program

Key Takeaways

  • Positioning: Google calls it its strongest model, aimed at cybersecurity and long-horizon agentic work; internal teams saw it run for days to root-cause and patch subtle bugs across large codebases and low-level systems
  • Official scores (evaluated without guardrails): 88.2% on SEC-bench Pro, 54.8% on ExploitGym and 71.0% on Collinear's CWE-bench; 59 and first place on the Artificial Analysis Intelligence Index v5.0; +89 to fourth place on WebDev Arena at 1769
  • Safety and alignment: successful cheating on Terminal-Bench 4.0 fell to 0.0% with a 0.4% attempt rate, high-severity misalignment alerts in internal live coding dropped by almost 80%, unsupported claims are about half of Argon's, and the Gray Swan indirect prompt injection attack success rate fell from Argon's 0.7% to 0.0%
  • Internal examples: more than 2x Argon's success rate at generating secure code, and the Chrome team reports more than 10x better high-quality patches than another lab's large frontier model
  • Rollout: a subset of cyber defenders in the Fairwind program first, alongside the U.S. government's voluntary pre-release testing; no developer API yet; introductory pricing of $2 / $10 per million tokens with cached input 95% off, then $4 / $20
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Last Secure Code Benchmark: Vibe Coding Under Fire as Top Agents Produce Secure Code on Only 25.1% of Tasks

While AI coding agents and 'vibe coding' workflows synthesize an unprecedented volume of production software, code security remains highly fragile. Researchers from NUS and Monash University introduced the Last Secure Code Benchmark, evaluating 450 tasks derived from 150 real CVE vulnerabilities across 7 languages (Java, C, C++, JS, Python, PHP, Go) and 24 CWE categories (including 21 of the 2025 CWE Top 25 and 8 of the Top 10 KEVs). While frontier agents achieve 73.6% to 91.3% functional completion rates, even the strongest model (Claude Fable 5) produces code that is both functional and secure on only 25.1% of tasks, exposing acute supply-chain risks in autonomous vibe coding.

Last Secure Code Benchmark: Vibe Coding Under Fire as Top Agents Produce Secure Code on Only 25.1% of Tasks

Key Takeaways

  • First comprehensive vibe-coding security benchmark: 450 tasks based on 150 CVEs spanning 7 languages, covering 21 of the 2025 CWE Top 25 and 8 Top 10 KEVs
  • The functionality illusion: while agents pass functional specs at 73.6%–91.3%, dual functional-and-secure pass rates collapse to 16.4%–25.1%
  • Vulnerability defense crisis: across full-repository and file generation scenarios, top models resolve only 12% of vulnerability environments securely
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CheckerBench: ECNU and Peking University Introduce the First Executable Benchmark for Coding Agents Synthesizing Static-Analysis Checkers

Prevailing coding agent benchmarks evaluate isolated patch synthesis (e.g. SWE-bench) or vulnerability detection, neglecting whether agents can construct reusable, production-grade static-analysis checkers from scratch. Developing static analyzers requires interpreting defect specifications, traversing multi-file AST semantics, writing engine-specific logic, and refining implementations through iterative compilation and diagnostic feedback. Researchers from East China Normal University, SJTU, and Peking University unveil CheckerBench (arXiv:2610.07557), an executable suite comprising 300 tasks derived from 297 CVEs across 167 repositories, 85 CWEs, and five programming languages. Powered by the CheckerLab harness, evaluation across 21 model-harness configurations reveals a mean Pass@1 rate of only 32.30% (peak 45.33%), demonstrating that end-to-end program analysis tool synthesis represents a profound new frontier for software engineering agents.

Key Takeaways

  • ECNU, SJTU, and Peking University launch CheckerBench, the first executable benchmark for coding agents synthesizing static analysis checkers
  • Features 300 tasks spanning 297 CVEs and 85 CWEs across C/C++, Go, Java, Python, and Rust with the CheckerLab framework
  • 21 model-harness setups achieve a mean Pass@1 of only 32.30% (peak 45.33%), exposing gaps in compiler-in-the-loop tool synthesis
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Google DeepMind launches Gemini 4 Argon: DeepSWE 77.9%, 1M output; Fairwind trusted defenders first

DeepMind announced frontier Gemini 4 Argon for long-horizon coding, enterprise knowledge work, and cyber defense—first via Fairwind trusted defenders. Official: DeepSWE v1.1 77.9%, AutomationBench #1 at 51.3%, LVBench 91.7%, CWE-bench v1 tied 68%; intro $2/$10 per 1M tokens; 1M output limit.

Google DeepMind launches Gemini 4 Argon: DeepSWE 77.9%, 1M output; Fairwind trusted defenders first

Key Takeaways

  • Model: Gemini 4 Argon for long-horizon coding / enterprise knowledge work / cyber defense — official blog
  • Benchmarks (official): DeepSWE v1.1 77.9% SOTA; AutomationBench 51.3% #1; LVBench 91.7%; CWE-bench v1 tied 68%; Fairwind charts: vuln discovery 85.8% / Wiz pen-test 70.9%
  • Capacity & price: 1M output tokens (from ~64K); intro $2/$10 per 1M in/out (~95% off cached input), then $4/$20
  • Access: Fairwind trusted defenders today; next paid API + Google AI Ultra; not in public AI Studio yet — Fairwind
  • Internal: quantum subroutine spacetime −40% vs published baseline; libgav1 Rust port 2.7× faster; 300+ TiB memory freed in datacenters
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Claude Security Public Beta: Mythos 5 Scans GitHub Repos, Patches Open in Claude Code

Anthropic moved Claude Security scans onto Claude Mythos 5, now in public beta for all Claude Enterprise customers. Point it at a GitHub repo and Mythos traces data across files, returning CWE, confidence, severity, and a suggested fix that opens in Claude Code on the web.

Key Takeaways

  • No separate Mythos 5 access required: the model runs behind the scan and bills as standard plan tokens
  • Findings include CWE, confidence, and severity; suggested patches open in Claude Code on the web
  • Paired with a $35M Defender Advantage Fund for open-source security and an expanded Cyber Verification Program
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