Security & IT News

Security researchers at Paradigm Shift have published a working exploit, dubbed usbliter8, that achieves arbitrary code execution inside the SecureROM of Apple's A12 and A13 chips. That code is burned into the silicon at manufacture. No software update can reach it. Affected devices will carry this flaw for as long as they stay in use. This is not a remote attack. It requires

This week's release includes five new modules, including a full unauthenticated RCE chain for Paperclip AI and a VS Code extension persistence technique. On the post-exploitation side, the new windows/local/ntlm_relay_2_self module coerces the local machine account to authenticate via OpenEncryptedFileRaw (WebDAV), relays that NTLM authentication to a Domain Controller's LDAP service, then uses the resulting LDAP session to write Shadow Credentials and obtain a Kerberos service ticket as Administrator via S4U2Proxy, enabling PsExec back to itself for SYSTEM access. On the enhancement side, the new MCP server plugin lets AI tools assist operators directly within a running msfconsole instance, and module check codes now return richer detail for users. New module content (5) Paperclip AI RCE using a chain of six API calls (CVE-2026-41679) Authors: Sagilayani https://github.com/sagilayani and h00die-gr3y [email protected] Type: Exploit Pull request: #21547 contributed by h00die-gr3y Path: linux/http/paperclipai_unauth_rce_cve_2026_41679 AttackerKB reference: CVE-2026-41679 Description: Adds an exploit module for CVE-2026-41679 which exploits Paperclip. An unauthenticated attacker can achieve full remote code execution on any network-accessible Paperclip instance running in authenticated mode with default configuration. The entire chain is six API calls. Xerte Online Toolkits Arbitrary File Upload - Unauthenticated Media Upload Author: bootstrapbool [email protected] Type: Exploit Pull request: #21371 contributed by bootstrapbool Path: multi/http/xerte_unauthenticated_mediaupload AttackerKB reference: CVE-2026-41459 Description: Exploits authentication failure ( CVE-2026-34413 ), extension blacklist ( CVE-2026-34415 ), and path traversal ( CVE-2026-34414 ) vulnerabilities in Xerte Online Toolkits versions 3.15 and earlier. VS Code Extension Persistence Author: h00die Type: Exploit Pull request: #21465 contributed by h00die Path: multi/persistence/vscode_extension Description: Adds a new persistence module that achieves persistence by installing a malicious extension into a user's VS Code extensions directory. The next time the target opens VS Code, the extension executes and delivers a shell back to the attacker. NTLM Relay to Self (HTTP to LDAP) - Post Exploitation Author: jheysel-r7 Type: Exploit Pull request: #21430 contributed by jheysel-r7 Path: windows/local/ntlm_relay_2_self Description: Adds a module that exploits the NTLMRelay2Self attack. It requires a low-privilege user session on a Windows host. Linux Kernel __ptrace_may_access() Exit Race Change File Disclosure Authors: 0xdeadbeefnetwork and bhaskarbhar Type: Post Pull request: #21472 contributed by bhaskarbhar Path: linux/gather/cve_2026_46333_chage AttackerKB reference: CVE-2026-46333 Description: Adds a post module that leverages CVE-2026-46333, a vulnerability in the Linux kernel whereby a race condition exists when tearing down a process. A local attacker can exploit this t

Microsoft has confirmed a confusing Windows bug that causes different filenames to appear in the confirmation dialog when deleting a file from the Recycle Bin. [...]

CISA has urged U.S. federal agencies to secure their systems by Sunday against a critical Splunk Enterprise vulnerability that is being exploited in attacks. [...]

In this article Attack chain overview Discovery and initial indicators Dependency injection: the poisoned package.json Typosquat analysis: easy-day-js Staged delivery pattern Obfuscation and payload analysis TLS bypass to self-deletion Timeline analysis Who is Sapphire Sleet? Mitigation and protection guidance Microsoft Defender XDR detections Microsoft Security Copilot Advanced hunting Indicators of compromise (IOC) References Learn more June 19, 2026 update: Microsoft assesses with high confidence that this activity is attributable to Sapphire Sleet , a North Korean state actor that primarily targets the financial sector. The infrastructure and post-compromise TTPs observed in this campaign are consistent with previously documented Sapphire Sleet activity. Sapphire Sleet also conducted a separate npm supply chain compromise affecting Axios , a popular JavaScript HTTP client, in April 2026 . Microsoft Threat Intelligence observed a large-scale npm supply chain attack affecting 140+ packages across the mastra and @mastra scopes on the npm registry. Microsoft shared its findings with the npm security team, the compromised packages have been removed and the attacker’s publish access to the @mastra scope has been revoked. The compromise originated from the takeover of the ehindero npm maintainer account, which had publish rights across the Mastra ecosystem and was used to publish poisoned package versions that introduced easy-day-js, a malicious typosquat of the popular dayjs library. Microsoft assesses with high confidence that this activity is attributable to Sapphire Sleet . Once installed, easy-day-js triggered a postinstall hook that executed an obfuscated dropper script, disabled Transport Layer Security (TLS) certificate verification, contacted attacker-controlled command-and-control (C2) infrastructure, downloaded a second-stage payload, and executed the payload as a detached hidden process. The activity followed a coordinated staged delivery pattern, with a clean bait version published first, followed by a weaponized version and rapid publication of the compromised Mastra packages. Because the payload executes during installation, any developer workstation or continuous integration and continuous delivery (CI/CD) pipeline that ran npm install or npm update after the compromised versions were published was potentially exposed, regardless of whether the package was imported in application code. This created risk to credentials, tokens, build environments, and downstream software integrity. Microsoft Defender Antivirus, Microsoft Defender for Endpoint, and Microsoft Defender XDR provide detections and hunting coverage for suspicious Node.js execution, malicious package behavior, reflective code loading, persistence activity and command-and-control communication. Attack chain overview Figure 1. End-to-end attack chain from npm account takeover through mass dependency injection to second-stage payload execution. At a high level, the attack

In this article From the lab into the pipeline This month’s set of discoveries Beyond the headline: What the engineering work taught us Where we go next Defense at AI speed Learn more Every vulnerability has two clocks running. One belongs to the defender racing to find it; the other to the cyberattacker hoping to find it first. For as long as software has existed, those clocks have favored the attacker, because modern code is vast, interconnected, and changing every day, while security reviews happen at fixed moments in time. The space between “code shipped” and “code reviewed” is where risk quietly accumulates. A few months ago, we set out to reshape that timing. We introduced codename MDASH , Microsoft Security’s multi-model agentic scanning system, built to discover, validate, and help remediate software vulnerabilities end-to-end. The goal was straightforward to articulate and hard to execute: take AI-powered vulnerability discovery and remediation capability from a research project and turn them into production-grade defense at enterprise scale. That meant going beyond pattern matching and building a system that could reason through the complexity of proprietary code and platforms like Windows, Hyper-V, Azure, and identity systems. Learn more about MDASH and sign up to join the preview Rather than rely on any single model, the system orchestrates a panel of specialized AI agents, each with its own role in a structured pipeline, so security teams can surface hard bugs quickly and systematically, expanding the reach of human-led review. Findings flow into Microsoft Defender workflows, where they can be prioritized alongside threat intelligence and runtime signals, and into GitHub and Azure DevOps pipelines, where they can be validated and remediated, a closed loop connecting discovery, validation, proof, and fix across the Microsoft stack. When we introduced the system, it topped a leading industry benchmark. That was the announcement, and the starting line. In the weeks since, the system has moved from early capability validation into active use by Microsoft engineering teams across Windows, Azure, and identity systems, applied as part of real security workflows rather than isolated testing environments. This post explores what we have built since, the lessons we’ve learned from turning research into a production-quality system, and the opportunities ahead as we focus on delivering real-world security impact. From the lab into the pipeline The most meaningful change since launch is where the system is being used. Engineering teams across Windows, Azure, and identity systems are now applying the system as part of their security workflows, running it alongside existing processes and reviews, targeting it at the surfaces that are hardest to audit manually and have historically required the most effort to cover. The goal is to use AI-driven analysis to go deeper, earlier, and across a broader set of targets than traditional approaches allo

We are excited to share that Microsoft has been named a Leader in The Forrester Wave™: Extended Detection and Response Platforms, Q2 2026 . Microsoft ranked the highest of any vendor evaluated in the Strategy category and is the only vendor to receive the highest score in Vision. Microsoft also received the highest possible scores across the current offering criteria of identity detection, cloud detection, SIEM replacement, Threat Intelligence, Threat hunting, Administrative controls, and Training. In the report, Forrester writes that “ Microsoft articulates a compelling vision to build a Frontier approach to security, bringing people and AI together while the platform continuously shields against and disrupts attacks.” Read the full Forrester Wave report A new frontier for XDR That recognition reflects how Microsoft sees the next phase of XDR evolution. As cyberattackers use AI to scale and accelerate their campaigns, defenders need more than correlated signals. They need a system that brings together data, people, and workflows so security can operate with the same speed and coordination. At Microsoft, XDR is that foundation. It connects signals across identities, endpoints, email, software as a service (SaaS) apps, and cloud workloads into a shared layer of context bringing together the signals, workflows, and actions security runs on. That foundation extends directly into how protection and operations are delivered. Microsoft Defender’s native capabilities continuously shield against attacks with built-in, system-level defenses, while embedded agents help triage alerts, hunt for threats, and deliver intelligence in the flow of work. The result is a shift from fragmented response to coordinated, system-level defense—where decisions, actions, and protection move together by default. Attack disruption is one of the clearest expressions of that vision today. It uses cross-domain signals and AI to stop multi-stage cyberattacks like ransomware and adversary-in-the-middle attacks while they are active and unfolding. Forrester specifically notes attack disruption in the report, “ As well as its roadmap, it (Microsoft) has built unique features, like automatic attack disruption, to help deliver on its vision.” Get started with Microsoft Defender World-class threat intelligence at the core Threat intelligence is a brand-new evaluation criterion in this Wave and Microsoft earned the highest possible score. This reflects a broader shift: intelligence is no longer a bolt-on, but fundamental to how modern XDR platforms detect, prioritize, and disrupt cyberattacks. Microsoft Threat Intelligence is built on a broad vantage point, analyzing 100 trillion signals each day. That intelligence is delivered directly into the analyst experience, which provides context on threat actors: their motivations and tactics appear inside incidents, alongside affected assets, and tied to response actions. The intelligence is built into detections, attack disruptio

In March, we wrote that identity security has become the new pressure point for modern cyberattacks . Since then, AI has only increased that pressure. AI helps cyberattackers move faster across the attack chain: personalizing social engineering at scale, automating reconnaissance, analyzing leaked credentials, identifying privileged users, probing exposed systems, and adapting tactics in real time. Attacks that once depended on manual effort can now unfold with greater speed, scale, and autonomy. Yet even as methods evolve, identity remains one of the most common entry points. Every account, admin, workload, application, non-human identity , and AI agent can become a path to sensitive data and critical systems if not properly secured. Attackers do not need to break every defense; they only need to compromise or misuse the right identity with the right access at the right moment. When attacks are accelerated by AI, speed and accuracy in detection and response are critical. Identity security can no longer operate in silos. Even a minor delay between when a threat is detected and action is taken can be the difference between suspicious activity becoming a contained incident or a business-impacting breach. This shift is reshaping how organizations think about security. The imperative is becoming clear: identity and security teams need comprehensive visibility and integrated solutions that streamline how they prevent, detect, and respond to identity threats. Securing the future of identity at the speed of AI One of the biggest security challenges organizations face today is fragmentation, and identity security is no exception. IAM and SOC teams often work across separate tools, separate workflows, and separate operational models. But identity attacks don’t respect those organizational boundaries. Modern identity attacks span infrastructure, access control, and detection. At Microsoft, we understand this, and we are continuing to expand how Microsoft Entra and Microsoft Defender work together to provide more unified identity security experiences. Actionable intelligence, everywhere At RSA earlier this year, we unveiled our unified identity risk score , a new way to turn broader attack-chain insight into real-time access decisions. This score analyzes and correlates relevant signals across related accounts, sessions, workloads, and applications to surface a single, comprehensive evaluation of an identity’s true risk level and enable more dynamic response directly within authentication flows as part of risk-based Conditional Access policies. View of a risky user within Entra ID Protection with new identity risk score and attack timeline. Identity admins also gain a stronger operational experience through the new Microsoft Entra ID Protection experience. Rather than forcing identity teams to piece together risk signals across disconnected views, the updated experience brings deeper visibility into risky users, sign-ins, workloads, and associated detection

Bad actors are exploiting multiple security vulnerabilities in Fortinet FortiSandbox, according to threat intelligence firm Defused Cyber. In a post shared on X, the company said it has observed exploitation of CVE-2026-39813, CVE-2026-39808, and CVE-2026-25089 over the past 24 hours. CVE-2026-39813 (CVSS score: 9.1) refers to a path traversal vulnerability in FortiSandbox JRPC API that could

Cisco has released security updates for a medium-severity security flaw in Catalyst SD-WAN Manager that has come under active exploitation in the wild. The vulnerability, tracked as CVE-2026-20262, carries a CVSS score of 6.5 out of 10.0. "A vulnerability in the web UI of Cisco Catalyst SD-WAN Manager, formerly SD-WAN vManage, could allow an authenticated, remote attacker to create a file or

Microsoft publishes quarterly email security benchmarking data comparing Microsoft Defender against secure email gateway (SEG) and integrated cloud email security (ICES) vendors using real-world threat telemetry. A year ago, we set out to change how email security effectiveness is measured. With our first benchmarking report in July 2025 , we committed to publishing real-world performance data, not synthetic tests, so security teams could make decisions grounded in evidence. With each quarterly update, we refined our methodology, expanded our analysis, and listened to customer and partner feedback. Alongside it, we established the Microsoft Defender ICES vendor ecosystem, designed to enable seamless integration with trusted third-party vendors and streamline security operations center (SOC) workflows for organizations who have chosen a multi-vendor email security strategy. Read the latest Microsoft benchmarking data for email security Key insights from a year of email benchmarking With four consecutive quarters, several findings have proven to be durable insights, highlighting the sustained realities of how layered email security performs in production: 1. Defender consistently leads in pre-delivery detection. Across every benchmarking period since July 2025, Defender has missed fewer high-severity cyberthreats than every SEG vendor evaluated, while the next closest SEG vendor had 2.5 times more misses. 2. ICES vendors add the most value in promotional and bulk email filtering. Promotional filtering uplift has been the clearest area of ICES value with an average uplift of 15% over the four quarters of evaluation. Meanwhile ICES vendor uplift for malicious catch and spam has consistently remained relatively nominal, averaging at 0.29% and 0.68%, respectively. In addition, over the last three quarters we’ve seen a consistent downward trend in these numbers, as we have continued to drive innovation in post-delivery mail detection. 3. Defender’s share of post-delivery remediation has grown significantly. In our second report, we introduced insights on the contribution of Defender to post-delivery malicious catch. Initially, Defender contributed 45% of post-delivery malicious catch, which has since risen to an average of 96%. This trajectory underscores that Microsoft’s post-delivery catch is an increasingly critical backstop, operating even when ICES solutions are in place, and that Defender is delivering the majority of post-delivery remediation. Figure 1: Malicious catch and spam catch uplift from ICES vendors of the past 12 months. SEG vendor benchmarking results For SEG vendors, a threat is classified as “missed” if it was not detected prior to delivery. Using this definition, Microsoft Defender once again missed fewer high-severity email threats than all other solutions evaluated, consistent with every prior benchmarking period. Figure 2: High-severity email threats missed by SEG vendors (February 2026-April 2026), measured as threats missed per

New Tracing Options As hard as we try to ensure that Metasploit is bug free, issues inevitably come up. Whether you’re running a module on an op or writing a new one, what we can do is make the debugging experience easier. To that end one of our two Google Summer of Code (GSoC) projects is here to deliver. Building on the previous pattern of HttpTrace comes two new options KerberosTicketTrace and CertificateTrace . These options, when enabled, will enable debugging output of Kerberos tickets and Certificates that are both sent and received by applicable modules. Now when things aren’t going quite right, users have new levers to reach for to inspect what’s happening under the hood. For example, to inspect exactly what’s happening when using the auxiliary/admin/kerberos/get_ticket module: msf auxiliary(admin/kerberos/get_ticket) set KerberosTicketTrace true KerberosTicketTrace = true msf auxiliary(admin/kerberos/get_ticket) run [*] Running module against 192.168.159.10 [*] 192.168.159.10:88 - Getting TGT for [email protected] #################### # Kerberos Request: AS-REQ #################### Protocol Version: 5 Message Type: 10 (AS-REQ) Pre-Authentication Data: Entry[0]: Type: 128 (PA_PAC_REQUEST) Value: [binary 7 bytes: 3005a0030101ff] Request Body: KDC Options: Value: 1082195984 Flags: - FORWARDABLE - RENEWABLE - CANONICALIZE - RENEWABLE_OK Client Name: Name Type: 1 (NT_PRINCIPAL) Name String: - smcintyre Realm: MSFLAB.LOCAL Server Name: Name Type: 1 (NT_PRINCIPAL) Name String: - krbtgt - MSFLAB.LOCAL Till: 2026-06-12T18:21:36Z Rtime: 2026-06-12T18:21:36Z Nonce: 6831592 Encryption Type: - 18 (AES256) - 17 (AES128) - 23 (RC4_HMAC) - 3 (DES_CBC_MD5) - 16 (DES3_CBC_SHA1) #################### # Kerberos Response: KRB-ERROR #################### Protocol Version: 5 Message Type: 30 (KRB-ERROR) Server Time: 2026-06-11T18:21:36Z Server Microseconds: 862696 Error Code: Name: KDC_ERR_PREAUTH_REQUIRED Value: 25 Description: Additional pre-authentication required Realm: MSFLAB.LOCAL Server Name: Name Type: 1 (NT_PRINCIPAL) Name String: - krbtgt - MSFLAB.LOCAL Error Data: [binary 87 bytes: 30553032a103020113a22b04293027301ea003020112a1171b154d53464c41422e4c4f43414c736d63696e747972653005a0030201173009a103020102a20204003009a103020110a20204003009a10302010fa2020400] #################### # Kerberos Request: AS-REQ #################### Protocol Version: 5 Message Type: 10 (AS-REQ) Pre-Authentication Data: Entry[0]: Type: 2 (PA_ENC_TIMESTAMP) Value: [binary 67 bytes: 3041a003020112a23a0438724f4965bd3deb1f061e807b616a09b613f59d9a6749eaee895e2ec3ed3045403cb28874acaa371681e3957a3ec23879141411ba788886f3] Entry[1]: Type: 128 (PA_PAC_REQUEST) Value: [binary 7 bytes: 3005a0030101ff] Request Body: KDC Options: 1350565888 Client Name: Name Type: 1 (NT_PRINCIPAL) Name String: - smcintyre Realm: MSFLAB.LOCAL Server Name: Name Type: 1 (NT_PRINCIPAL) Name String: - krbtgt - MSFLAB.LOCAL Till: 2026-06-12T18:21:36Z Rtime: 2026-06-12T18:21:36Z Nonce: 7068778 Encryption Type:

A 10-year-old authentication bypass vulnerability discovered in the phpBB forum software allows an attacker to log in as any user, including administrators. [...]

NPM, part of GitHub, announced a new version of the npm package manager with several security improvements, including disabling install scripts

Microsoft has fixed a known issue that caused Windows updates released since May 2025 to fail when installed via the Windows Update Standalone Installer (WUSA) from a network share. [...]

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) ordered government agencies to patch an actively exploited Ivanti Sentry flaw within three days, as mandated by the newly issued Binding Operational Directive (BOD) 26-04. [...]

New CISA directive tells federal agencies to patch by real-world risk, not CVSS severity scores

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) announced a new Binding Operational Directive, 26-04, that prioritizes security updates for Federal Civilian Executive Branch (FCEB) agencies. [...]

Microsoft has resolved a known issue causing some Windows Server 2025 devices to boot into BitLocker recovery after installing the April 2026 security update. [...]

Today, we’re releasing Adaptive Spec-driven Scoring for Evaluation and Regression Testing (ASSERT) , an open-source framework for turning natural-language behavior specifications into executable evaluations. Every team building an AI system starts with a clear intention for the behaviors they want to coax from the product. Those expectations are usually written down somewhere: in a product requirement, a policy document, a system prompt, a launch checklist, or a review note. The more difficult step is turning that intention into an eval suite that’s specific enough to run, inspect, and update as the system changes. ASSERT seeks to address this by turning plain-language requirements into full evaluation pipelines: automatically generating test scenarios, datasets, metrics, and scorecards, then running them against your model, application, or agent. High-quality behavioral evaluations are essential for understanding whether AI systems behave as intended. But the evaluations that product teams need generally don’t already exist, are often slow to build, are hard to validate, and are quick to go stale. Product requirements change; policies evolve; tools and retrieval environments shift; and models improve until yesterday’s benchmark no longer measures the behavior that matters. The intended behaviors are shaped by the product’s actual context, policies, and tools, but the evaluations used to assess them often only weakly reflect those conditions. The gap is most visible in application-specific behavior. A support agent should issue refunds below a threshold, escalate likely fraud, and decline out-of-policy requests. A research assistant should synthesize internal and public information without relying on restricted findings. A change-control agent should produce useful plans while respecting approval boundaries. Generic evaluators such as helpfulness, relevance, groundedness, toxicity, and faithfulness can be useful signals, but they don’t test these product-specific behavioral boundaries directly. A system can score well on generic metrics while failing application-specific requirements ASSERT is built on the premise that a behavior specification should be a first-class input to evaluation—not just the background context. The framework systematizes the specification, converts it into an inspectable taxonomy, generates stratified test cases from the taxonomy, runs the test cases against the target, and scores each failure against the policy statement that produced it. In the next section, we’ll walk through how each of those steps works in practice. How ASSERT works The pipeline has four stages. First, ASSERT turns a broad behavior specification into an explicit concept specification, which is then converted into a granular, editable behavior taxonomy with suggested permissible and impermissible behaviors. Next, it generates stratified test cases over the dimensions the developer declares. Then, it runs those cases against the target system and recor