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The npm package 'express-enrouten-async' is a malicious package impersonating the legitimate 'express-enrouten' library. Instead of providing legitimate route discovery functionality, it hardcodes demo routes and declares a dependency on a node-fetch package hosted on a third-party, non-npm registry. This causes npm installs to fetch attacker-controlled code, enabling arbitrary code execution. Systems with this package installed should be considered fully compromised, and all secrets and keys must be rotated. Removal of the package alone does not guarantee full remediation due to potential persistent compromise.

As frontier AI models collapse the traditional exploit window, Tenable Hexa AI transforms the security operating model from manual triage to agentic orchestration. See how you can automate vulnerability remediation and super-charge exposure management with Tenable Hexa AI. Key takeaways AI models like Claude Mythos have reduced the time from vulnerability discovery to weaponization from weeks to minutes, making manual defense untenable. Tenable Hexa AI serves as an agentic engine that orchestrates complex, multi-step remediation workflows across modern attack surfaces to accelerate the speed of preemptive security and propel your exposure management program. Using the Model Context Protocol (MCP) included in Tenable Hexa AI, your team can build and deploy custom agents that anchor your preferred LLMs in the Tenable Exposure Data Fabric, ensuring every automated action is governed, auditable, and accurate. Why you need to implement agentic AI in cybersecurity (and specifically, in vulnerability management) For most of my career in cybersecurity, we’ve operated on a fundamental, if unspoken, assumption: We had a grace period. Whenever a new vulnerability was discovered, we knew we had time, often weeks or months, before adversaries would begin exploiting it. The time between vulnerability discovery and exploitation gave us breathing room. It gave us time to patch, triage, and remediate. But not any more. The gap between discovery and exploitation has been shrinking for years, and the vulnerability discovery capabilities demonstrated by frontier AI models like Claude Mythos are narrowing it even more. We have entered the era of AI speed. When an LLM can unearth a 27-year-old vulnerability in a hardened OS in minutes, and then weaponize it in seconds, old defensive cycles can’t keep up, and that’s untenable. This is why I’m so excited to announce the general availability of Tenable Hexa AI , the agentic engine of the Tenable One Exposure Management Platform , at EXPOSURE 2026 : because it’s designed to help your organization address the escalating, AI-driven pace of vulnerability discovery. The agentic AI imperative in cybersecurity: Scale your preemptive defense to match machine speed with agentic innovation from Tenable Tenable Hexa AI is built to be a force multiplier and a flexible engine for innovation. Featuring a suite of built-in agents ready to automate assessment configuration, asset tagging, dashboard creation, ticket creation, and more, Tenable Hexa AI is designed to help your organization overcome the operational challenges deepened by adversarial AI use. When the window between discovery and exploitation hits near-zero, security teams locked in manual vulnerability management operating models are forced into a state of perpetual emergency. Manually stitching together context and telemetry from cloud, identity, OT, and vulnerability silos in an arduous effort to prioritize remediation for downstream IT and DevOps teams is a losing battle. And when you can’t provide clear, risk-based remediation priorities to IT and DevOps teams, you end up bombarding them with seemingly urgent tickets that may not in fact be critical to your organization. Constant shifts in remediation priorities and endless debates over what needs fixing and why is not sustainable. It creates friction and causes you to lose the cybersecurity race. In a world where attackers move at machine speed, only comprehensive exposure intelligence combined with the agentic AI orchestration capabilities provided by the Tenable One Exposure Management Platform can give you clarity and control. Tenable Hexa AI doesn’t just tell you where you are vulnerable; it mobilizes your preemptive defense. Capabilities of Tenable Hexa AI With this GA release, Tenable delivers foundational capabilities to help your organization accelerate the pace of vulnerability discovery and remediation, including: Your choice of agents - Use our pre-built, out-of-the-box agents to start reducing risk immediately, or use the Model Context Protocol (MCP) server built into Tenable Hexa AI to create custom agents tailored to your organization’s environment. Advanced multi-step reasoning - Tenable Hexa AI executes complex, end-to-end workflows spanning your attack surface (e.g., IT, cloud, identity, OT, etc.) in a single request, eliminating the need for practitioners to toggle between views to get exposure context. It understands that a CVE in your web app is a critical threat specifically because it is linked to a privileged service account with a path to your sensitive data. Automated remediation workflows - Tenable Hexa AI orchestrates remediation workflows, automatically creating and routing tickets, generating custom policies, and producing audit-ready reports, so security teams can act fast on every critical exposure. End-to-end exposure path insights - Practitioners can query their environment by identity attributes, such as service accounts, privileged users, and Active Directory groups, to surface exposure paths that traditional asset inventories miss. Tenable Hexa AI also provides guided assistance for complex Active Directory sensor configurations. Build your own AI agents for cybersecurity with Tenable Hexa AI In addition to out-of-the-box agentic capabilities for use cases like automated assessment configuration, asset tagging, and ticket creation, customers can also build custom agents via Tenable Hexa AI's built-in MCP that are informed by your organization’s unique security policies and internal business logic. Tenable Hexa AI serves as the orchestration layer connecting your favorite AI tools to your infrastructure and other security tools, all with the data and context from the Tenable Exposure Data Fabric. By anchoring the models your organization uses in the authoritative context of your own environment, Tenable Hexa AI moves you beyond generic AI answers to governed and auditable automation. Whether you are automating complex remediation or generating board-ready dashboards, Tenable Hexa AI ensures the output is both verifiable and auditable. The Tenable Exposure Data Fabric is key because an agent is only as effective as the data it has access to. Tenable Hexa AI is powered by the Tenable Exposure Data Fabric, a repository of 20 years of vulnerability research and the industry’s largest collection of contextualized exposure data. In other words, we’ve built an agentic engine for cybersecurity that uses the world’s best exposure data to drive machine-speed actions. This is the only way to ensure your AI is validating the real state of your environment, rather than just guessing. Real-world agentic AI use cases for Tenable Hexa AI While there are virtually infinite ways to apply agentic orchestration to your unique cybersecurity challenges, here are four high-impact areas where manual workflows traditionally break down and make it impossible for you to keep pace with AI-powered vulnerability discovery: Supply chain response - Neutralize third-party threats by using Tenable Hexa AI to correlate software components with affected internal assets. Automated patching - Use custom Hexa agents to beat the Mythos clock by orchestrating patches the moment a vulnerability is validated. Remediation assignment - Use Tenable Hexa AI to automatically match CVEs to asset owners in seconds and trigger immediate response workflows. These use cases demonstrate how Tenable Hexa AI can bridge the gap between exposure intelligence and action. Make the untenable Tenable The collapse of the exploit window is a wake-up call. It gives us the opportunity to change how we work. By shifting from manual triage to agentic orchestration, organizations are seeing a shift in productivity and how they prioritize and action exposure reduction. While early design partners have already reclaimed days per month on foundational tasks like asset tagging, the value is not found solely in the hours saved, but rather, in the precision of the response. By automating the correlation between cloud, identity, AI, OT, and vulnerability data, Tenable One provides the clear, contextualized instructions that IT and DevOps teams need to act with confidence. This eliminates the administrative friction and back-and-forth negotiation that often results in critical vulnerabilities going unaddressed. Reclaiming those days means your best people are no longer buried in spreadsheets; they are focused on high-impact strategy, architecture hardening, and preemptive defense. Tenable Hexa AI is available today as part of the Tenable One Foundation and Tenable One Advanced packages .

The 2026 Verizon Data Breach Investigations Report (DBIR) reveals that vulnerability exploitation has become the leading initial access vector for breaches, responsible for 31% of incidents. Meanwhile, organizations are patching vulnerabilities more slowly, with median time-to-patch increasing by 34% to 43 days. The volume of vulnerabilities continues to grow rapidly, driven in part by AI-powered tools that accelerate vulnerability discovery. This creates a widening gap between vulnerability disclosure and remediation, exacerbating the risk of exploitation. The report highlights the critical need for comprehensive exposure management that prioritizes risks based on context rather than attempting to patch all vulnerabilities indiscriminately. It also notes that credential abuse and misconfigurations compound the exposure problem. The findings underscore a systemic challenge in traditional patch-based defense models, especially as AI accelerates both vulnerability discovery and exploitation.

In this week’s newsletter, Martin reflects on what the next iteration of AI tools means for vulnerability discovery and our ability to manage large-scale patch releases.

GitHub Copilot CLI versions prior to 1.0.43 contain a high-severity vulnerability (CVE-2026-45033) that allows arbitrary code execution. This occurs when a malicious bare git repository is nested inside a project directory, exploiting git's automatic bare repository discovery during directory traversal. The vulnerability leverages git configuration keys such as core.fsmonitor and others that can execute arbitrary shell commands during normal git operations. This issue is fixed in version 1.0.43.

This analysis addresses the evolving security challenges faced by data centers in the era of agentic AI, where AI systems autonomously execute business decisions. Data centers have become critical national infrastructure, integrating legacy operational technology (OT) and extended IoT (xIoT) devices that were not originally designed with modern security in mind. The threat landscape includes AI-powered attacks targeting both digital assets and physical infrastructure, such as cooling and power systems, potentially causing operational denial and costly downtime. Attackers may exploit vulnerabilities in third-party vendor portals or engineering workstations to pivot into process control layers, disrupting physical systems. The complexity of securing data centers is heightened by the need to maintain near-perfect uptime while managing rapid patch cycles and protecting identity-based network communications. Regulatory frameworks increasingly require continuous, real-time monitoring and proactive exposure management across IT, OT, identity, and AI domains. A comprehensive security strategy involves asset discovery, attack path anticipation, risk prioritization, identity perimeter protection, third-party risk monitoring, and compliance streamlining.

Affected devices do not properly restrict access to the web browser via the Control Panel when no corresponding security mechanisms are in place. This could allow an unauthenticated attacker to gain unauthorized access to the web browser, potentially enabling the discovery of backdoors, performing unauthorized actions, or exploiting misconfigurations that may lead to further system compromise.

A sophisticated multi-stage infection chain was identified through proactive threat hunting, beginning with the execution of MicrosoftToolkit.exe, a commonly abused hack tool. The attack employed file masquerading techniques, renaming a .dot file to .bat format to evade detection. The malware performed process discovery and attempted to terminate security-related processes before extracting payloads using extract32.exe. An AutoIt-compiled executable (Replies.scr) functioned as a loader, processing an external encrypted payload file and establishing command-and-control communication with infrastructure associated with Vidar Stealer. The malware demonstrated advanced anti-analysis capabilities, including debugger detection and instrumentation callback queries. It targeted credentials, browser data, cryptocurrency wallets, and system information. Post-execution cleanup routines deleted artifacts and terminated processes to minimize forensic evidence and evade detection, significantly complicating incident res...

ZEBRA is a Zcash node written entirely in Rust. Prior to version 4.4.0, a composite denial-of-service vulnerability in Zebra's block discovery pipeline allows an unauthenticated remote attacker to permanently halt all new block discovery on a targeted node. The attack exploits three independent weaknesses in the gossip, syncer, and download subsystems — all exercisable from a single TCP connection — to create a monotonically growing block deficit that never self-heals. This issue has been patched in version 4.4.0.

### Impact A database query used during proxy API key checks mixed the caller-supplied key value into the query text instead of passing it as a separate parameter. An unauthenticated attacker could send a specially crafted `Authorization` header to any LLM API route (for example `POST /chat/completions`) and reach this query through the proxy's error-handling path. An attacker could read data from the proxy's database and may be able to modify it, leading to unauthorised access to the proxy and the credentials it manages. ### Patches Fixed in **`1.83.7`**. The caller-supplied value is now always passed to the database as a separate parameter. Upgrade to `1.83.7` or later. ### Workarounds If upgrading is not immediately possible, set `disable_error_logs: true` under `general_settings`. This removes the path through which unauthenticated input reaches the vulnerable query. ### References - Patched release: [`v1.83.7-stable`](https://github.com/BerriAI/litellm/releases/tag/v1.83.7-stable) **Discovery Credit**: Tencent YunDing Security Lab

Between July 2025 and present, threat actors suspected to be OceanLotus distributed malicious wheel packages through PyPI targeting both Windows and Linux platforms. Three fake libraries (uuid32-utils, colorinal, and termncolor) were created to imitate legitimate packages, implementing a sophisticated supply chain attack. The packages deployed droppers that delivered ZiChatBot, a previously unknown malware family using Zulip's REST APIs as command and control infrastructure instead of traditional C2 servers. The malware supports executing shellcode commands and establishes persistence through registry keys on Windows or crontab on Linux. Attribution to OceanLotus is based on 64% similarity with known droppers analyzed by KTAE system. The malicious packages were swiftly removed from PyPI following discovery.

North Korea-aligned APT group ScarCruft executed a multiplatform supply-chain attack targeting ethnic Koreans in China's Yanbian region, an area significant for North Korean refugees and defectors. Since late 2024, the group compromised a video gaming platform dedicated to Yanbian-themed games, trojanizing both Windows and Android components with the BirdCall backdoor. The Windows client received malicious updates leading to RokRAT and subsequently BirdCall deployment, while Android games were directly trojanized. This marks the first discovery of Android BirdCall, capable of comprehensive surveillance including data collection, screenshots, and voice recording. The campaign focuses on espionage against individuals of interest to the North Korean regime, particularly refugees and defectors.

Cisco Talos identified an intrusion active since at least January 2026 involving the CloudZ remote access tool (RAT) combined with a previously undocumented plugin named 'Pheno'. The Pheno plugin is reported to potentially steal one-time password (OTP) messages, indicating targeted information theft capabilities. No specific affected software versions or patches are currently documented. There is no evidence of known exploits in the wild beyond this discovery. The threat is assessed as medium severity based on the available information.

A vulnerability in the Linux kernel's IPv6 Neighbor Discovery (ndisc) component was fixed by zeroing uninitialized padding fields in the nduseroptmsg structure. These padding fields, when processing Router Advertisements with user options, could leak kernel memory data. The issue affects specific Linux kernel versions and has a CVSS score of 7.1, indicating a medium severity. A patch is available and has been incorporated in updated kernel releases.

ZionSiphon is operational technology-focused malware targeting water treatment and desalination facilities in Israel. The sample demonstrates ICS-awareness through industrial protocol interaction capabilities including Modbus, with incomplete support for DNP3 and S7comm. It incorporates geographic and environmental validation controls designed to restrict execution to Israeli water infrastructure systems. The malware attempts persistence through registry autorun entries, privilege escalation, and removable media propagation. Functionality includes network discovery of industrial devices, process manipulation targeting chlorine dosing and flow control, and configuration file modification. A critical validation flaw prevents successful execution, suggesting the analyzed sample represents incomplete development or testing. Embedded pro-Iran and anti-Israel messaging indicates politically motivated intent, though no specific threat actor attribution exists.

CVE-2026-31682 is a critical vulnerability in the Linux kernel's bridge component where the function br_nd_send() improperly handles neighbor discovery options. The function assumes these options are in a linear buffer, but callers only guarantee linearity of the ICMPv6 header and target address, potentially causing out-of-bounds memory access. This flaw has been fixed by ensuring the request is linearized before parsing the options.

Improper Input Validation, Improper Control of Generation of Code ('Code Injection') vulnerability in Apache ActiveMQ Broker, Apache ActiveMQ All, Apache ActiveMQ. An authenticated attacker may bypass the fix in CVE-2026-34197 by adding a connector using an HTTP Discovery transport via BrokerView.addNetworkConnector or BrokerView.addConnector through Jolokia if the activemq-http module is on the classpath. A malicious HTTP endpoint can return a VM transport through the HTTP URI which will bypass the validation added in CVE-2026-34197. The attacker can then use the VM transport's brokerConfig parameter to load a remote Spring XML application context using ResourceXmlApplicationContext. Because Spring's ResourceXmlApplicationContext instantiates all singleton beans before the BrokerService validates the configuration, arbitrary code execution occurs on the broker's JVM through bean factory methods such as Runtime.exec(). This issue affects Apache ActiveMQ Broker: before 5.19.6, from 6.0.0 before 6.2.5; Apache ActiveMQ All: before 5.19.6, from 6.0.0 before 6.2.5; Apache ActiveMQ: before 5.19.6, from 6.0.0 before 6.2.5. Users are recommended to upgrade to version 5.19.6 or 6.2.5, which fixes the issue.

Frontier AI models represent a new generation of advanced large-scale AI capable of autonomously identifying software vulnerabilities, chaining exploits, and adapting to defenses at machine speed. This accelerates the discovery and weaponization of vulnerabilities, compressing attack windows from days to minutes. Open-source software is particularly at risk due to its transparent codebase, enabling large-scale supply chain compromises. Vulnerability chaining allows attackers to combine multiple lower-severity issues into critical exploit paths, bypassing traditional security filters. Current human-speed security operations are insufficient to keep pace with autonomous AI-driven attacks, necessitating AI-driven detection and response. Frontier AI also enhances reconnaissance and social engineering by generating highly personalized phishing attacks. Defenses must shift to integrating AI into development and security processes, adopting adaptive identity and access management, and prioritizing vulnerability triage based on attacker reachability and AI exploitability. Palo Alto Networks offers services to help organizations assess and prepare for these emerging risks.

Unit 42 reports that frontier AI models significantly enhance the discovery of software vulnerabilities by acting as autonomous security researchers. These AI models can identify zero-day vulnerabilities and accelerate the patching of known issues (N-day vulnerabilities). This development represents a shift in software security dynamics, potentially fracturing traditional security approaches by enabling faster and more comprehensive vulnerability discovery. There is no indication of specific affected software versions or known exploits in the wild at this time. The threat is assessed as medium severity based on the potential impact of accelerated vulnerability discovery.

An open directory discovery at refundonex[.]com exposed a complete Phishing-as-a-Service and RAT-as-a-Service platform targeting Spanish and Portuguese-speaking victims. The investigation uncovered 3,788 files including weaponized LNK, VBS, and AES-encrypted PowerShell payloads delivering a remote access trojan. The platform, called Shadow Panel, operates from Bulgarian infrastructure and offers capabilities including remote shell execution, screenshot capture, file management, browser credential theft, clipboard hijacking for cryptocurrency wallets, and multi-operator support. The C2 panel's frontend JavaScript was publicly accessible, revealing 29 API endpoints and the complete architecture. Infrastructure analysis linked the operation to nikola4010@proton[.]me through WHOIS data and historical malicious domain associations dating back to 2021, indicating a long-running cybercriminal operation with minimal detection coverage.