ZeroHour
DashboardNewsCVEsExploitsIncidentsAIBriefAnalyticsEntitiesWatchlistIOCsAsk the feedSourcesFeeds & APIKiosk mode

Search: “format string”

3 stories in the last 24h

Searchkeywordsmart Sortlatesthotimportancerelevance
DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllVulnerability 2AI tools & infra 1Research 1
SeverityAllcritical 0high 1medium 0low 2info 1

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 · 4h agofirst · 14h agoVulnerability 5 sources

Telegram Desktop XSS Vulnerability Lets Attackers Steal Entire Chat Histories

Stored XSS in Telegram Desktop HTML chat exports (CVSS 8.2) could let attacker-controlled inline keyboard buttons steal full chat histories.

ExPatch researchers Denis and Aleksander Rostilov found a stored XSS in Telegram Desktop's HTML chat export pipeline affecting builds before Beta 6.9.4 and Stable 7.0.1. Unsanitized inline keyboard button text becomes executable JavaScript when a user exports a chat and opens the HTML file in a browser, exposing messages, metadata, and local file paths, and enabling phishing overlays. Telegram patched the issue in commit 8457d13a during July 2026; no CVE had been assigned at disclosure time.

GBHackers · 19h agoVulnerability

Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend

MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.

The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.

MarkTechPost · 7h agoAI tools & infra