CVE-2026-104419

The Zebra (zebrad) software, versions 4.5.0 before 6.3.0 , is vulnerable to a logic flaw in how it handles FindBlocks responses. The software fails to track which peer provided specific block hashes, leading it to assign 100 misbehavior points (the ban threshold) to any peer that serves a block m...

CVE-2026-104419 Zebra before 6.3.0 Honest Peer Banning via Far-Ahead FindBlocks Hashes

Zebra zebrad 4.5.0 before 6.3.0 discards which peer supplied the block hashes in FindBlocks responses, then assigns 100 misbehavior points, the ban threshold, to whichever peer serves a requested block more than 50,000 heights above the tip. A remote peer can return real far-ahead hashes to a...

CVE-2026-104419 Zebra before 6.3.0 Honest Peer Banning via Far-Ahead FindBlocks Hashes

Zebra zebrad 4.5.0 before 6.3.0 discards which peer supplied the block hashes in FindBlocks responses, then assigns 100 misbehavior points, the ban threshold, to whichever peer serves a requested block more than 50,000 heights above the tip. A remote peer can return real far-ahead hashes to a...

EUVD-2026-91430

Zebra zebrad 4.5.0 before 6.3.0 discards which peer supplied the block hashes in FindBlocks responses, then assigns 100 misbehavior points, the ban threshold, to whichever peer serves a requested block more than 50,000 heights above the tip. A remote peer can return real far-ahead hashes to a...

CVE-2026-104419 Zebra before 6.3.0 Honest Peer Banning via Far-Ahead FindBlocks Hashes

Zebra zebrad 4.5.0 before 6.3.0 discards which peer supplied the block hashes in FindBlocks responses, then assigns 100 misbehavior points, the ban threshold, to whichever peer serves a requested block more than 50,000 heights above the tip. A remote peer can return real far-ahead hashes to a...

Why CISOs Struggle to Answer the Board's Three Hardest Questions, and How to Fix the Report

The quarterly board meeting is two weeks out. The security team is pulling exports from the identity provider, the cloud posture tool, the vulnerability scanner, the SIEM and the EDR console. Someone is building a spreadsheet to reconcile them. Someone else is turning that spreadsheet into slides...

CVE-2026-104419: Insufficient Verification of Data Authenticity

Zebra zebrad 4.5.0 before 6.3.0 discards which peer supplied the block hashes in FindBlocks responses, then assigns 100 misbehavior points, the ban threshold, to whichever peer serves a requested block more than 50,000 heights above the tip. A remote peer can return real far-ahead hashes to a...

PT-2026-104230

Name of the Vulnerable Software and Affected Versions Zebra zebrad versions 4.5.0 through 6.2.0 Description The software fails to track which peer supplied block hashes in FindBlocks responses. It assigns 100 misbehavior points, which is the ban threshold, to any peer that serves a requested bloc...

CVE-2026-12544

A flaw exists in Foreman within the foreman-rake initialization logic located in /usr/share/foreman/config/settings.rb. The vulnerability stems from a code pattern where configuration data is processed through two distinct executable layers, creating a multi-stage execution chain. This allows for...

AI-Infra-Guard v4.6.4

📖 Documentation | 🌐 🇨🇳 中文 · 🇯🇵 日本語 · 🇪🇸 Español · 🇩🇪 Deutsch · 🇫🇷 Français · 🇰🇷 한국어 · 🇧🇷 Português · 🇷🇺 Русский 🚀 AI Red Teaming Platform by Tencent Zhuque Lab A.I.G AI-Infra-Guard integrates capabilities such as ClawScanOpenClaw Security Scan, Agent Scan，AI infra vulnerability scan...

xss_db

RUBY RUBY는 웹 요청의 공격 가능성을 탐지하고, 위험도와 정책에 따라 대응 전략을 선택해 방어 기법을...

EUVD-2025-209815

An untrusted pointer dereference in the ionic cloud driver for VMWare ESXi could allow an attacker with an unprivileged VM to read kernel memory or co-located guest VM memory, potentially resulting in loss of confidentiality or availability...

A Deterministic and Auditable AI Security Risk Assessment Framework with ATLAS Aligned Executable Rules and Formal Verification

Artificial intelligence systems are increasingly deployed in high impact and safety critical settings, yet security assessment remains difficult to reproduce and defend under audit. Existing approaches often rely on narrative checklists or assessor driven scoring, and they lack an explicit, machi...

Sleeping Secrets: How Fine-Tuning Reawakens Privacy Risks in Language Models

Beyond adapting Large Language Models LLMs to specialized applications, fine-tuning has recently been shown to recover private information that is no longer accessible through direct queries. Previous fine-tuning recovery attacks, however, require genuine private supervision drawn from the same...

CVE-2026-101883 OpenClaw Windows Node through 2026.9.4 SSRF via canvas.present

OpenClaw Windows Node through 2026.9.4 contains a server-side request forgery vulnerability in the canvas.present capability that bypasses URL risk evaluation enforced by canvas.navigate. Attackers with gateway or agent access can issue canvas.present to make the node's WebView send requests to...

Malicious code in reactjs-risk (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 0e58c82916c5199ef9e73a5d70f91d120312151eb5c28090d8bbf6dbf4117543 The package's preinstall lifecycle script collects host identity metadata from the installer machine — os.hostname, the output of...

MAL-2026-17339 Malicious code in reactjs-risk (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 0e58c82916c5199ef9e73a5d70f91d120312151eb5c28090d8bbf6dbf4117543 The package's preinstall lifecycle script collects host identity metadata from the installer machine — os.hostname, the output of...

Malicious Package

Overview reactjs-risk is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this package...

Speculative Safety Honeypot: Toward Proactive Defense against Multi-Turn Agent Attacks

As Large Language Model LLM agents are increasingly deployed in complex environments, multi-turn interaction attacks have become a significant security challenge. Existing detection methods typically rely on historical context. However, this retrospective logic struggles to identify deep maliciou...

HARDE: Optimizing Agent Harnesses for Runtime Risk Detection and Execution Control

Large language model LLM agents are vulnerable to safety risks such as injected malicious instructions or misleading information, motivating runtime defenses that prevent unsafe action in execution across diverse risks while preserving benign-task utility. Existing system-level defenses either...