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GhostCode Abuses Microsoft Entra Device Enrollment to Maintain Access After Token Revocation
eSentire exposes GhostCode, a device-code phishing kit that abuses Microsoft Entra device enrollment to persist even after stolen tokens are revoked.
eSentire's Threat Response Unit observed GhostCode campaigns in late August 2026, using BEC-style social engineering that impersonated procurement staff, including BJ's Wholesale Club, via Salesforce contact forms. Victims received password-protected HTML lures disguised as a FlipBook document portal, with junk-data padding, HTML comment injection, and AES-256-GCM encrypted redirects gated by anti-bot checks. The kit exploits the OAuth 2.0 device authorization grant, prompting victims to approve real Microsoft device-code sign-ins with MFA. Within 78 seconds of approval, attackers registered three Entra devices and obtained a Primary Refresh Token, so rogue device registrations persist even after session token revocation.
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When Agents Slow Down: Understanding LLM Agents' Test-Time Strategies via Elo-per-token Analysis
Elo-per-token analysis shows LLM agents' marginal gains drop below independent sampling at scale; parallel sessions beat one long session.
The paper proposes Elo-per-token analysis, using a Bradley-Terry model to measure how agent performance scales with token budget on open-ended tasks with continuous scoring. Across four agents and four benchmarks with sessions up to 100M tokens, agents initially convert tokens to Elo faster than independent sampling but eventually slow below the linear-in-log-compute reference. The authors define a scaling inflection point and show that splitting 100M tokens across parallel sessions on FrontierCS Polyomino Packing gains +264 Elo over one long session and +355 over ten short sessions. Human contestants on shared AtCoder Heuristic Contest tasks improve superlinearly, indicating headroom over current agents.
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Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses
Quesma benchmarks Qwen3.8 27B quantizations: 4-bit Q4_K_M matches BF16 on key benchmarks while 1-bit collapses to random chance.
Quesma spent roughly $3,000 on Modal GPUs testing Unsloth GGUF quantizations of Qwen3.8 27B across GPQA Diamond, IFBench, and Terminal-Bench 2.1. The 17 GB Q4_K_M quantization matched the 55 GB BF16 model on Terminal-Bench 2.1 and showed little degradation down to 4-bit, while the 2-bit UD-Q2_K_XL dropped noticeably. At 1-bit, scores on GPQA Diamond fell to random-guess levels, with longer reasoning making results worse, and reasoning effort settings significantly affected outcomes.
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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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UAT-10608: Inside a large-scale automated credential harvesting operation targeting web applications
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