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5 stories in the last 24h

We got admin access to Baseten's production GitHub in 25 minutes

Strix autonomous hacking agent extracted a working GitHub token with repo admin rights from Baseten's public Harbor image; Baseten rotated it next day.

Strix, an autonomous hacking agent, scanned *.baseten.co without credentials and found a public Harbor container registry project anonymously exposing the baseten/baseten-app image. A GitHub personal access token for basetenbot, embedded in Docker build history since March 2023, still worked in July 2026 and granted admin/push rights to basetenlabs/baseten, flux-cd, and homebrew-tap plus read/write on private customer repos. Baseten, valued at $13 billion, confirmed the issue as critical and rotated the token within a day.

Red Heron Hackers Exploit Critical Gitea RCE to Steal Source Code and Deploy Linux Rootkit

PRC-linked Red Heron exploits critical Gitea RCE CVE-2026-60004 to steal source code and deploy JITTERLY implant with SIXZUT LD_PRELOAD rootkit; victims span five countries.

Acronis Threat Research Unit attributes a campaign to Chinese-speaking threat actor Red Heron, which weaponized CVE-2026-60004, a CVSS 9.8 RCE in Gitea versions 1.17 through 1.27.0, patched in 1.27.1 on July 27, 2026. The actor built an automated exploitation framework after a public PoC appeared, scanned 1,386 internet-exposed Gitea instances across seven countries, and separately listed 477 Taiwan-based systems across defense, energy, elections, and AI sectors. Confirmed victims include organizations in Canada, Argentina, Taiwan, the US, and Sri Lanka, with a Canadian renewable-energy firm hit in 22 sessions and a Taiwanese industrial automation firm losing hundreds of repositories including SCADA/HMI tools. Red Heron deploys the JITTERLY Linux implant (30+ commands, AES-128-GCM, Adaptix-like protocol) and the SIXZUT LD_PRELOAD rootkit disguised as libglthread.so.2, and moved laterally into a Synology/Proxmox environment to steal VM backups.

GBHackers · 18h agoThreat actor in the wild 3 sourcesCVE-2026-600045

Saving Jet Fuel

Tutorial optimizes flight paths to cut jet fuel costs using open-source Scikit-decide planning framework and OpenAP aircraft performance models.

A technical walkthrough demonstrates wind-aware flight path optimization using Scikit-decide, an open-source framework for reinforcement learning and automated planning, paired with OpenAP fuel-consumption models built by Dr. Junzi Sun at TU Delft and NOAA wind data. A Boeing 787-9 flying EWR to FCO can require roughly $68K in fuel, and adjusted routing could save thousands. The post uses Python 3.12, DuckDB with spatial extensions, and QGIS for map rendering.

Re: Retrospective by 'gpg.fail' authors

GnuPG's Werner Koch says gpg.fail samples only crash GnuPG via DER-as-printf format string in --debug x509; RCE claim remains unproven.

Werner Koch replied to the gpg.fail retrospective, noting that GnuPG versions above 2.2 produce garbled stderr or crash when the project's certificates are used with --debug x509 because DER data is passed as a printf format string. Testing the certificates from the researchers' Git repo yielded only a segfault, not demonstrated code execution. Koch states how remote code execution would be achieved is unclear and asks for a real reproducer.

oss-securityupdated · 31m agofirst · 14h agoVulnerability 6 sources

How much of F-Droid is LLM generated?

A FOSS maintainer manually graded 102 F-Droid apps from the September 12, 2026 update batch, finding many show signs of LLM-generated code.

A student and FOSS app maintainer reviewed 102 apps pushed to F-Droid on September 12, 2026, assigning each a three-tier rating for likelihood of LLM-authored code (mostly AI >50%, hard to say/mostly human, no signs of AI). The heuristic relies on commit aesthetics, README and branding style, and the presence of agentic infrastructure like Claude Code or Codex, which automatically places an app in the 'mostly AI' tier. Example ratings include Amber (Nostr event signer) as mostly AI, and Aria for Misskey as showing no AI signs. The author stresses reliable detection of LLM-generated code from text alone is impossible, so ratings are approximate.