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ZDI-26-673: Adobe Acrobat Pro DC Doc Object Use-After-Free Remote Code Execution Vulnerability

ZDI disclosed CVE-2026-81988, a use-after-free in Adobe Acrobat Pro DC document object handling that enables remote code execution.

Zero Day Initiative advisory ZDI-26-673 describes a use-after-free vulnerability in the document object handling of Adobe Acrobat Pro DC. Arbitrary code execution is possible when a target opens a malicious file or visits a malicious page. The flaw received a CVSS rating of 7.8. No in-the-wild exploitation is reported.

ZDI Published Advisories·Sep 10, 05:00 UTC · 7d agoVulnerability 19 sourcesCVE-2026-81988

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ZDI-26-598: Foxit PDF Reader AcroForm Use-After-Free Remote Code Execution Vulnerability

Foxit PDF Reader AcroForm use-after-free (CVE-2026-57242, CVSS 7.8) permits remote code execution through malicious PDF files or pages.

ZDI-26-598 details a use-after-free in the AcroForm component of Foxit PDF Reader, tracked as CVE-2026-57242 and rated CVSS 7.8. A remote attacker can execute arbitrary code if the target opens a malicious file or visits a malicious page. The advisory was published by the Zero Day Initiative on August 24, 2026.

ZDI Published Advisories·Aug 24, 05:00 UTC · 24d agoAdvisoryCVE-2026-57242

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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.

Hacker News · security·Sep 16, 02:07 UTC · 1d agoResearchHN 25↑ · 18 comments

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ZDI-26-597: Foxit PDF Reader AcroForm Use-After-Free Remote Code Execution Vulnerability

Foxit PDF Reader has a use-after-free vulnerability (CVE-2026-57252, CVSS 7.8) allowing remote code execution via malicious AcroForm content.

ZDI-26-597 describes a use-after-free vulnerability in the AcroForm feature of Foxit PDF Reader, tracked as CVE-2026-57252 with a CVSS score of 7.8. Successful exploitation allows remote attackers to execute arbitrary code, but requires user interaction such as visiting a malicious page or opening a malicious file. The advisory does not mention any exploitation in the wild.

ZDI Published Advisories·Aug 24, 05:00 UTC · 24d agoVulnerabilityCVE-2026-57252

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Apple’s new iPhone camera mode promises to prove your photo isn’t AI

Apple's iPhone 18 Pro adds a Reference Image mode that cryptographically signs camera sensor pixels to prove photos were not AI-generated or edited.

Apple will launch a Reference Image mode with the iPhone 18 Pro lineup, using the new camera sensor to sign every pixel and develop the signed data via Private Cloud Compute into an unalterable reference image viewable in Photos. Users can compare the reference image against edited versions to verify authenticity, building on provenance standards like SynthID, C2PA, and Meta's Content Seal. A Reference Image API will span iOS, iPadOS, and macOS for third-party apps, though the feature launches without EU support, arriving there in iOS 27, iPadOS 27, and macOS 27.

The Verge · AI·Sep 9, 19:30 UTC · 7d agoAI industry

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DriveZero: End-to-End Driving Beyond Human Demonstrations

DriveZero pairs a frozen vision-foundation-model perception stack with a PPO-trained closed-loop RL teacher to beat replay experts on nuPlan.

DriveZero is an end-to-end camera-only autonomous-driving planner that separates perception and action. Its DriveVFM perception backbone consolidates frozen vision foundation models (DINOv3, SigLIP2, SAM, Depth Anything V2) from raw images without task annotations, while DriveRL trains a privileged PPO teacher policy through closed-loop rollouts in interactive worlds built from real driving logs. The planner distills this teacher, achieving a 93.57 mean nuPlan score across Val14, Test14-hard and Test14-random splits and beating the Log-Replay expert on all three. It also sets state of the art on NAVSIMv1, NAVSIMv2 and closed-loop HUGSIM without human trajectory supervision.

Hugging Face daily papers·Sep 4, 20:00 UTC · 12d agoAI research

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SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection

SlipSense fuses a 32x32 piezoresistive array and MEMS accelerometer to detect robotic grip slips within 23.1 ms, generalizing zero-shot across platforms.

SlipSense is a multimodal tactile slip-detection framework built on TacV5, a sensor combining a 32x32 piezoresistive array at 240 Hz and a 3-axis MEMS accelerometer at 8 kHz. It performs modality-specific encoding, intra-sensor fusion, and cross-modal attention with causal temporal prediction at 240 Hz. On a 1.4-million-frame dataset spanning 37 objects it achieves 96.7% Macro F1 with a false-positive rate below 1.6%, detecting 76% of slip events within 23.1 ms. Trained solely on UMI data, it transfers zero-shot to a Tesollo dexterous hand across unseen objects, sensor units, and platforms.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:23 UTC · 2d agoAI research

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AVP-Inspect: Coordinated Cyber-Physical Testing for Privacy Analysis of COTS Apple Vision Pro Applications

AVP-Inspect automated testing finds 58% of 324 Apple Vision Pro apps show privacy violations, with over 60% of network traffic flows undisclosed.

Researchers built AVP-Inspect, a dynamic analysis framework combining custom hardware device control, 3D UI exploration, and a unified privacy taxonomy for Apple Vision Pro. Testing 324 App Store apps for 20 minutes each found 188 (58.0%) with at least one privacy violation. More than 60% of observed network traffic flows were not properly disclosed, extending prior XR privacy work beyond Android-based devices such as Meta Quest.

arXiv cs.CR·Sep 8, 01:24 UTC · 9d agoResearch1

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More Americans oppose police license plate cameras than support them: survey

A survey finds more Americans oppose police license plate cameras than support them amid reported police abuses of surveillance cameras.

A new survey shows opposition to police license plate reader cameras now exceeds support among Americans. The backlash comes amid a wave of reported police abuses involving surveillance cameras. The findings add to public debate over police surveillance technology.

TechCrunch · Security·Aug 28, 15:57 UTC · 19d agoPolicy & legal

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ThinkPrior: Zero-Rollout Difficulty Priors for Cold-Start Prompt Selection in RLVR

ThinkPrior builds zero-rollout difficulty priors via an offline verifier-anchored pass, halving silent groups in RLVR and cutting wasted rollouts on Qwen2.5-Math-7B.

In GRPO-based RLVR, groups where all rollouts are correct or all are wrong yield zero advantages and consume about 39% of a run's rollouts under uniform sampling. ThinkPrior initializes a Beta posterior from an external anchor pass's verifier-scored pass rate, selecting prompts by expected learnability before any target-policy rollout, without changing the loss or optimizer. On Qwen2.5-Math-7B across sixteen seeds it more than halves early silent groups and cuts wasted rollouts through step 30 by nearly a fifth, with no detected final-accuracy difference. The ThinkPrior+DAPO composition reduces generated rollouts by 10.6% at an equal 3,840-rollout update budget.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:30 UTC · 8d agoAI research1

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TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking

Researchers propose TFTrack, a template-free LiDAR 3D single object tracking framework cutting FLOPs ~50% while running at ~120 FPS.

TFTrack is the first template-free framework for 3D Single Object Tracking, dropping template-search pairings and complex motion modeling in favor of the prior bounding box center plus geometric alignment. It ships in three variants (TFTrack-Voxel, TFTrack-Pillar, TFTrack-Point) covering sparse and dense 3D representations. On KITTI and nuScenes it is competitive with leading template-based trackers while reducing FLOPs by about 50% and running near 120 FPS. Code is released, targeting real-time deployment in embedded robotics such as autonomous vehicles.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 16:43 UTC · 9d agoAI research

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[webapps] Probo 0.222.2 - IDOR

A public proof-of-concept exploit was released for an IDOR vulnerability in web application Probo 0.222.2.

Exploit-DB published exploit #52650 targeting Probo version 0.222.2. The flaw is an Insecure Direct Object Reference (IDOR) in this web application. No exploitation in the wild is reported in the disclosure text.

Exploit-DB·Aug 17, 00:00 UTC · Aug 17, 2026Exploit / PoC

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Apple has a new way prove your iPhone photos aren’t AI slop

Apple launched Reference Image, cryptographically signing iPhone 18 Pro photos via Private Cloud Compute to prove image authenticity.

Announced at Apple's Surprise and Shine event, Apple Reference Image captures signed sensor data with the iPhone 18 Pro camera and uses Private Cloud Compute to create an unalterable 'digital negative' viewable in Photos. The reference image can be compared with edited versions to verify authenticity, and developer APIs enable third-party integration. Apple also said it will support the SynthID standard to identify AI-created or altered images.

TechCrunch · AI·Sep 9, 18:08 UTC · 7d agoAI industry

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