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ZDI-26-587: Ashlar-Vellum Cobalt VS File Parsing Heap-based Buffer Overflow Remote Code Execution Vulnerability
ZDI advisory ZDI-26-587 details a heap-based buffer overflow RCE (CVE-2026-19781, CVSS 7.8) in Ashlar-Vellum Cobalt VS file parsing, requiring user interaction.
The Zero Day Initiative released advisory ZDI-26-587 covering a heap-based buffer overflow in Ashlar-Vellum Cobalt's VS file parsing. A remote attacker can execute arbitrary code when the target opens a malicious file or visits a crafted page. ZDI rated the vulnerability CVSS 7.8 and assigned CVE-2026-19781.
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GPT-5.6 Luna vs. GPT-6 Astra: Is a $1.20 Model Good Enough for Code Review?
Entelligence benchmarks GPT-5.6 Luna ($1.20/M output) against GPT-6 Astra for code review: Luna found 69 verified bugs at 3.6% of Astra's cost.
Entelligence compared GPT-5.6 Luna ($0.20/$1.20 per million tokens) against GPT-6 Astra ($10/$50) on 50 benchmark pull requests from Cal.com, Sentry, Discourse, Keycloak, and Grafana. Astra verified 92 bugs versus Luna's 69, with precision of 96% versus 74%, and Astra caught 19 of 24 security bugs while Luna found only 9. Luna cost $0.20 total versus Astra's $5.66 and reviewed faster at 23 seconds versus 36, with the widest quality gap on Keycloak authentication and permission logic (6 vs 14 verified bugs). Running both models would find 82% of the 143 verified bugs for $5.86 total.
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[AINews] GPT-6 Astra: OpenAI’s biggest LLM launch of all time
OpenAI launched GPT-6 Astra, its new flagship model, claiming state-of-the-art computer use, software engineering, math, and cybersecurity capabilities.
OpenAI launched GPT-6 Astra as its new flagship model, describing it as its most intelligent and aligned model with state-of-the-art computer use, software engineering, and math/science capabilities. Pricing is $10/$50 per 1M input/output tokens standard ($20/$100 fast tier), rolling out first to limited organizations, then ChatGPT Plus/Pro/Business/Enterprise, the API, and AWS. OpenAI claims 99.9% on ARC-AGI-3, 98% on FrontierMath Tier 4, and 100% on ExploitBench. Artificial Analysis scored Astra 67 on the Coding Agent Index and 61 on the Intelligence Index, behind Claude Fable 5.1, and the system card drew attention for reporting decreased chain-of-thought monitorability despite alignment gains.
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Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models
Empirical study finds direct whole-file generation beats iterative diff-based editing for Flutter/Dart code models on about 1,790 held-out tasks.
Researchers trained Rainbow-Pony-100M from scratch and fine-tuned Qwen2.5-Coder-0.5B in both direct-generation and diff-based regimes, then evaluated four resulting models on roughly 1,790 Flutter/Dart tasks. Direct generation outperformed diff-based generation on compilation pass rate, bits-per-byte, character-level similarity, and blinded LLM-judge ratings. Diff-based editing is competitive only on short, localized edits in refactoring and error-handling tasks, a property the authors call task locality.
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Rare Not Random Using Token Efficiency for Secrets Scanning
Researcher proposes token efficiency (string length divided by BPE token count) as a better post-regex filter than entropy for secrets scanning, validated on CredData.
The post explores whether Byte-Pair Encoding tokenization can replace Shannon entropy as the primary filter for candidate secrets captured by regex in tools like Gitleaks. It defines 'token efficiency' as string length divided by token count under the cl100k_base tokenizer; secret-like strings such as GitHub tokens tokenize into many small tokens and score low, while natural text scores high. Evaluating labeled secrets from the CredData dataset shows a usable separation, with roughly 2.5 suggested as a minimum cutoff versus Gitleaks' 3.5 entropy threshold. The technique is positioned as a post-regex filtering step rather than a standalone detector.
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Google fixed actively exploited kernel zero
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Top 10 Best Cloud Security Posture Management (CSPM) Tools in 2026
2026 CSPM comparison ranks Wiz atop cloud posture tools and recaps Google's pending roughly $32 billion acquisition of Wiz.
An editorial guide rates ten cloud security posture management (CSPM) tools, with Wiz ranked first for agentless visibility and attack-path context, Microsoft Defender for Cloud highlighted for Azure-centric economics, and Palo Alto Prisma Cloud noted for the broadest code-to-cloud module set. The article's biggest market note is Google's agreement to acquire Wiz for approximately $32 billion, described as the largest deal in security history, still progressing through regulatory review. It advises buyers to include roadmap-protection language in multi-year commitments and to press on multicloud neutrality post-close.
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