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House passes bill to equip local law enforcement with scam-fighting tools
The U.S. House passed the GUARD Act, letting local law enforcement use federal grants to investigate financial scams and trace stolen cryptocurrency.
The bipartisan GUARD Act (Reps. Zachary Nunn, Scott Fitzgerald, Josh Gottheimer) passed the House, allowing existing DOJ grant funds to be used for fraud analysts, victim-support training, blockchain tracing software, and financial-information sharing with law enforcement. It addresses scams like pig butchering, often run by transnational criminal groups overseas; Americans lost a record $11.4 billion to crypto-related fraud in 2025, including $8.6 billion in investment fraud. Senators Katie Britt and Kirsten Gillibrand introduced a Senate companion in July 2025, and the House also passed a bill retroactively eliminating the 'scam tax' on stolen funds for 2021-2025 victims.
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Parallels Desktop flaw hands any local user root on a Mac (CVE-2026-90894)
CVE-2026-90894 in Parallels Desktop for Mac lets any local user gain root via argument injection; patched in v27.0.0, PoC withheld.
JFrog researchers disclosed CVE-2026-90894, an argument injection flaw in Parallels Desktop for Mac v26.4.0 on Apple silicon that lets any local user gain root on the host. The chain combines a world-writable Unix socket for prl_disp_service (which runs as root), weak peer-credential authentication, and argument injection via --use-compress-program in the appliance extraction tar path. Alludo fixed the flaw in Parallels Desktop v27.0.0 in early September 2026; JFrog published technical details but withheld its proof-of-concept script.
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Linux Kernel ZcopyReaper Vulnerability Lets Local Attackers Gain Root Privileges
Linux kernel RDS zero-copy flaw CVE-2026-43502 (ZcopyReaper) lets unprivileged local users gain root; fix in 7.1-rc3, public PoC published.
CVE-2026-43502, dubbed ZcopyReaper, is a Linux kernel local privilege escalation flaw in the RDS zerocopy send path, present since kernel 4.17, allowing unprivileged local attackers to gain root. NebuSec researcher Yuan Tan demonstrated root escalation on openSUSE kernel 6.4.0-150600.23.100; the fix landed in commit 44b550d88b26 with Linux 7.1-rc3 the first patched mainline release. Exploitation requires CONFIG_RDS and CONFIG_RDS_TCP but not unprivileged user namespaces, and PoC exploit material is publicly released alongside more than 20 other exploitable 2026 kernel CVEs.
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TrajMark: Ownership Attribution and Segment-Level Tamper Localization for Coding-Agent Trajectories
Researchers introduce TrajMark, a training-free watermarking framework for coding-agent trajectories that recovers ownership, detects 95.5-100% of edits, and localizes tampered regions.
TrajMark is a training-free, symmetric-key, visible-only watermarking framework for coding-agent trajectories that separates robust ownership attribution from fragile local integrity verification. A sparse owner layer encodes a six-bit deployment identifier by rewriting keyed READ actions into masked linear equations, while a localization layer inserts linked Q12 seals that commit to protected critical-action segments. Across three coding-agent frameworks and three LLMs, it recovers the exact owner in all clean full-watermark batches, detects 95.5%-100% of single-site edits, and localizes 95.8% of random corruptions to an accepted protocol region. Owner marking adds no trajectory actions and matched Pass@1 is 26.9% versus 26.3% for unwatermarked runs.
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Sakana AI Researchers Introduce PC-ALM, a Layer-Local Alternative to Backpropagation That Trains 1000-Layer Networks
Sakana AI's PC-ALM adds per-layer Lagrange multipliers to predictive coding, matching backprop on networks up to 1000 layers with layer-local updates.
Sakana AI researchers propose Augmented Lagrangian Predictive Coding (PC-ALM), a training method that keeps every update layer-local while recovering backprop-aligned credit signals. The team proves multipliers converge to exact backprop adjoints in linear networks and trains 1000-layer residual MLPs on MNIST within about 2 points of backprop accuracy. PC-ALM matched backprop across a width/depth grid from 8 to 128 on MNIST and Fashion-MNIST where standard predictive coding failed in deep, narrow networks, and improved over PC on ResNet-18 with CIFAR-10 and Tiny ImageNet. An MIT-licensed JAX reference implementation reproduces the results on CPU.
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Linux Detection Engineering - Local Privilege Escalation
Elastic details a layered detection framework for Linux local privilege escalation, covering 2026's copy-on-write bug wave and LLM-assisted discovery.
Elastic Security Labs describes how most Linux local privilege escalations share a common host flow — an unprivileged process launched from a writable path becoming root — and proposes layered detections combining general outcome-based rules with per-technique rules in Elastic Defend and Auditd. It tracks 13 recent LPE disclosures, seven of which share a copy-on-write/zero-copy bug class, including Copy Fail, DirtyFrag, Fragnesia, DirtyDecrypt, DirtyClone, pedit COW, and RefluXFS. Qualys attributes RefluXFS to an LLM-assisted research effort with Anthropic using Claude Mythos Preview, and another bug is credited to an LLM-assisted workflow. Detection and endpoint rules are published in Elastic's detection-rules and protections-artifacts repositories.
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