Microsoft Issues Patches for In-the-Wild 0
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Top AI spenders cut per-employee costs by nearly 10 percent in August
Ramp's September AI Index shows top AI spenders' per-employee costs fell 9.7% in August as firms migrate from frontier models to cheaper standard models.
Ramp's September 2026 AI Index reports median per-employee AI spending at the top 1% of spenders fell 9.7% in August to $7,205, partly attributed to August vacations, falling token prices, and migration to cheaper models. The effective price per million tokens dropped 41% from its March 2026 peak to $0.68, and frontier models like Opus, Fable, and Sol fell from 53% to 45% of tokens consumed. Anthropic was paid for by 43.8% of US companies (up 0.34 points) versus 39.8% for OpenAI (up 0.09 points), while open-weight models remain marginal at 6.4% of AI-using firms.
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PentestGPT: Open-source automated penetration testing agentic framework
Open-source PentestGPT runs autonomous LLM-driven penetration tests via Claude Code and Codex, with legacy human-in-the-loop mode supporting many providers.
PentestGPT, originally published at USENIX Security 2024 by Gelei Deng and colleagues, is an open-source framework that lets a large language model autonomously run penetration testing stages (recon, exploit, walkthrough) with no human in the loop, driving Claude Code or Codex CLIs. A legacy interactive mode uses three cooperating LLM sessions maintaining a Pentesting Task Tree and supports OpenAI, Anthropic, Google Gemini, DeepSeek, xAI, Qwen, Moonshot, and local models via Ollama. The tool sends anonymous telemetry to Langfuse by default, excluding command outputs, credentials, and flags, and is available free on GitHub.
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Why don't machine learning research agents overfit?
Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.
Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.
Hacker News · security32
Apple Reference Image: A New Approach for Verified Photography
Apple introduces Reference Image, hardware-backed verifiable photography on iPhone 18 Pro using sensor signing and Private Cloud Compute to counter AI-generated fakes.
Apple announced Reference Image, an opt-in camera mode debuting on the main sensor of iPhone 18 Pro and iPhone 18 Pro Max that produces securely timestamped, verifiable photographs. The design splits into two phases: a secure digital negative created by cryptographically signing pixel data at the sensor immediately after capture (preventing injection or tampering), then developing that negative into a reference image. Private Cloud Compute handles processing without exposing image contents to anyone, including Apple, and fraudulent reference images can be revoked without revealing the photographer's identity. Apple positions the system as stronger than C2PA-based approaches, which sign metadata after capture, are vulnerable to editing-chain compromise, and can tie images to a device or individual.
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Containers on fire: from container escapes to supply chain attacks
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Malicious Twitch Extension Exposes OAuth Tokens of 30,000 Chrome and Firefox Users
Malicious 'Twitch Enhanced Viewer | JeetBot' browser extension stole live OAuth session tokens from roughly 31,000 Chrome and Firefox users.
Socket.dev found the cross-store extension 'Twitch Enhanced Viewer | JeetBot' rerouted Twitch playlist requests through operator-controlled proxies, attaching the victim's account-level OAuth token as a URL parameter where it could be logged in cleartext. About 30,000 Chrome and 552 Firefox installs were exposed, with tokens forwarded for nearly every watched channel to infrastructure tied to a Russian commercial bot service. The stolen bearer token enables chat, whisper, account-setting access and channel-point spending without the password or 2FA.
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UK Council Attack Linked to Mass Exploitation of SonicWall Flaw
Hunt.io links a UK council attack to mass exploitation of SonicWall SMA1000 flaw CVE-2026-15409 (CVSS 10.0), enabling credential and Active Directory theft.
Hunt.io links, with moderate confidence, the July 17, 2026 attack on the Borough Council of King's Lynn and West Norfolk to mass exploitation of SonicWall SMA1000 appliances via CVE-2026-15409, an unauthenticated SSRF in the WorkPlace portal WebSocket proxy with CVSS 10.0. The operator adapted Rapid7's July 15 PoC into a 50-thread mass scanner within days and stole LDAP credentials for 534 Active Directory accounts across 160 domains, with nine environments losing SAM/LSA secrets and five losing full AD databases via DCSync. CISA added the flaw to its Known Exploited Vulnerabilities catalog and noted use in ransomware campaigns; targeting of ~200,000 Shodan-derived SonicWall addresses spanned government, healthcare, finance, universities, and manufacturing worldwide.
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ModaLens: Measuring Image Sensitivity in Report-Conditioned Medical VLMs
ModaLens image-swap audit shows report availability cuts MedGemma-27B image sensitivity on MIMIC-CXR from 20.94% to 4.26% answer changes.
ModaLens is a paired image-swap audit measuring how report availability affects image sensitivity in report-conditioned medical VLMs. On MedGemma-27B across 3,199 paired MIMIC-CXR cases from 293 patients (14 questions per case), generated answers changed on 4.26% of image-swap trials with the report versus 20.94% without it, a 16.7-point paired difference (95% CI 15.6-17.7). The original prompt with a lowercase first-token readout gave 4.70% versus 17.07%, and the direction replicated in two further model lineages. Labels derived from reports limit conclusions about visual correctness; code, prompts, and run records are publicly released.
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