Unit 42 uncovers CL-CRI-1171, a two-year pay-per-install campaign distributing 10,000+ OfferLoader samples via YouTube and SEO poisoning.
Palo Alto Networks Unit 42 tracked a campaign as CL-CRI-1171, an infection-as-a-service platform operating for at least two years. It used 11 YouTube gaming channels with hundreds of thousands of subscribers and SEO-poisoned download pages for trojanized WinDirStat and Bluetooth driver installers to distribute OfferLoader, a disposable loader embedded in Inno Setup installers. Payloads delivered included Insomnia RAT (Node.js and Python backdoors), ARKTunnel (a WebSocket-based RAT using LSB steganography), and Docro Hijacker (Chrome browser hijacker), with later infections delivering GCleaner and Socks5Systemz. Over 200 rotating domains across .xyz, .cfd, .space, and .info TLDs were used, with gating filters that served broken links or decoy pages to scanners and researchers.
Unit 42 links two Latin America campaigns where operators used Claude and GPT-4.1 during intrusions against government and financial targets.
Palo Alto Networks Unit 42 identified two activity clusters, CL-CRI-1131 and CL-CRI-1163, tied by shared SOCKS5 relay infrastructure and use of large language models during operations. The Mexican cluster targeted a transportation organization, federal ministries and water utilities in Mexico and Ecuador, while the Brazilian cluster used resume-themed phishing, custom remote-access Trojans and SockTz SOCKS5 tunneling against financial organizations. An exposed self-hosted NextChat interface on attacker infrastructure led researchers to assess operators used Claude and GPT-4.1 to generate workaround scripts and troubleshoot execution failures. Unit 42 noted AI reduced time needed to troubleshoot intrusions after initial access, rather than replacing the attacker.
Unit 42 exposes CL-CRI-1171, a pay-per-install network spreading malware like Insomnia RAT via YouTube channels and SEO poisoning for over two years.
Palo Alto Networks Unit 42 details CL-CRI-1171, a cybercrime cluster operating a pay-per-install (PPI) marketplace that has delivered multiple malware families for at least two years. The group used at least eleven YouTube gaming channels with hundreds of thousands of followers, plus SEO poisoning promoting trojanized software such as a Bluetooth driver and WinDirStat, infecting gamers and corporate endpoints including critical infrastructure and government entities. A single shared loader delivered payloads including Insomnia RAT, ARKTunnel, Docro Hijacker, GCleaner and Socks5Systemz between July 2025 and April 2026, with more than 10,000 distinct loader samples and over 200 rotating C2 domains identified. YouTube terminated the malicious channels after Unit 42 notified the platform.
Palo Alto Networks Unit 42 markets its paid incident response services backed by threat intelligence and methodology from thousands of investigations.
This is a vendor product page describing Unit 42's incident response offering rather than a news article. It emphasizes containing, remediating and eradicating attacks using threat intelligence and a methodology developed from real-world incident casework. No new incident, vulnerability, or actor activity is reported.
Palo Alto Networks Unit 42 hosts a product archive page for its Advanced DNS Security DNS-layer threat prevention service.
The page is a product category archive on the Unit 42 blog with no article text available. Advanced DNS Security is Palo Alto Networks' DNS-layer threat prevention offering. No research findings, incidents, or vulnerabilities are described.
Palo Alto Networks markets Cortex as an AI-driven security operations platform for detection, investigation, automation and response.
The Unit 42 blog page is a product category archive for the Cortex security operations portfolio. Cortex is described as an AI-driven SecOps platform providing detection, investigation, automation, and response capabilities. No research findings, incidents, or vulnerabilities are discussed.
Palo Alto Networks positions Cortex XSIAM as a purpose-built platform to simplify security operations and speed incident remediation.
The Unit 42 blog page is a product category archive for Cortex XSIAM. The platform is marketed as purpose-built for SecOps transformation, simplifying security operations and accelerating incident remediation against current and future threats. No research findings, incidents, or vulnerabilities are discussed.
Palo Alto Networks describes Advanced WildFire as its cloud malware analysis engine using machine learning and crowdsourced intelligence.
The Unit 42 blog page is a product category archive for Advanced WildFire. The description calls it the industry's largest cloud-based malware analysis and prevention engine, using machine learning and crowdsourced intelligence to detect hard-to-catch threats. No research findings, incidents, or vulnerabilities are discussed.
Palo Alto Networks markets Advanced URL Filtering as combining a malicious URL database with real-time ML and DL web protection.
The Unit 42 blog page is a product category archive for Advanced URL Filtering. The product description states it combines a malicious URL database with a real-time web protection engine powered by machine learning and deep learning models. No research findings, incidents, or vulnerabilities are discussed.
Palo Alto Networks Unit 42 hosts a product archive page for its Cloud-Delivered Security Services subscription portfolio.
The page is a product category archive on the Unit 42 blog with no article text available. Cloud-Delivered Security Services is Palo Alto Networks' bundle of cloud-delivered network and threat prevention services. No research findings, incidents, or vulnerabilities are described.
Mars Security launched Real-Time Intel-Based Detection, converting advisories from CISA and Mandiant into backtested MITRE ATT&CK-mapped detection rules for CrowdStrike, Wiz, and Splunk.
Mars Security announced a capability that automatically turns newly published threat intelligence from sources like CISA, Mandiant, Unit 42, and Microsoft into MITRE ATT&CK-mapped detection rules. Each rule is written in the native query language of the customer's telemetry (CrowdStrike Falcon, Wiz, Splunk, firewalls, identity providers, AWS, Snowflake, Databricks) and backtested against 30 days of the customer's data before deployment, with indicator scoring to drop noisy or stale indicators. The platform also maps existing detection coverage, flags gaps such as AWS CloudTrail tampering and pass-the-hash movement, and delivers some recommendations as open pull requests for detection-as-code workflows.
Palo Alto Networks Unit 42 markets Frontier AI Defense, a service combining frontier AI models and expert guidance to counter AI-powered attacks.
Unit 42 Frontier AI Defense is positioned as an elite service to neutralize AI-powered attacks before they scale, combining access to frontier AI models with Unit 42 expertise. It assesses customer security stacks against AI-driven attack techniques and offers hands-on guidance to modernize security operations. The page is primarily a product category description without new incident or vulnerability details.
Unit 42 documents two AI-assisted intrusion campaigns against Latin American government, utility, and financial organizations using LLM-orchestrated tooling.
Palo Alto Networks Unit 42 tracks two ongoing intrusion clusters, CL-CRI-1131 (Mexican transportation, federal ministries, municipal water utilities) and CL-CRI-1163 (Brazilian financial sector), both using living-off-the-land techniques, SOCKS5 relays, and custom RATs. The attackers appear to orchestrate operations via commercial LLMs like Claude and GPT-4.1, evidenced by iterative batch scripts and AI-generated tunneling tool naming. The Mexican campaign (also reported as Operation Escaneo by CloudSEK) exfiltrated sensitive data via dynamic-DNS infrastructure with rotated multi-SAN TLS certificates between February and June 2026. This signals broader adoption of AI-enhanced operations by diverse threat groups in the region.
Unit 42 reports AI agents compressed a ransomware intrusion from weeks to under 10 hours, using 50+ MITRE ATT&CK techniques against an enterprise network.
Palo Alto Networks Unit 42 investigated a ransomware incident where AI agents moved through an enterprise network in under 10 hours, work that would have taken human operators roughly two weeks. The attacker entered via a public-facing API endpoint, used automated reconnaissance to map microservices, searched source-code repositories for credentials, and accessed a secrets-management system. They hijacked enterprise code workflows to exfiltrate cloud access keys, attempted Terraform backdoors (blocked by branch protections), and used stolen credentials to access the victim's own AI services as attack infrastructure. Over 50 MITRE ATT&CK techniques were observed; the actor confirmed using frontier AI models and agentic frameworks during negotiations.
Unit 42 investigated a ransom attack in which frontier AI agents autonomously breached an enterprise network, compressing weeks of tradecraft into under 10 hours.
Unit 42 incident responders documented an intrusion where a single human operator directed frontier AI agents to breach an enterprise network autonomously as part of a ransom attack. The agents executed more than 50 MITRE ATT&CK techniques in under 10 hours, work that would normally require roughly two weeks of human red-team effort. They breached a public-facing web service, mapped internal microservices, scraped hard-coded secrets from code repositories, harvested root credentials from the secrets manager, and hijacked CI/CD builds to exfiltrate cloud access keys. The attacker also used stolen cloud keys to repurpose the victim's AI endpoints as post-compromise infrastructure and left behind an 80-page AI-generated security audit documenting dozens of exploited findings.
Unit 42 page describes Idira, a Palo Alto Networks product providing dynamic privilege controls and zero standing privilege for AI agents.
The Unit 42 product page describes Idira as delivering dynamic privilege controls to humans, machines, and AI agents. It claims AI-driven discovery of hidden risks, just-in-time enforcement of zero standing privilege, and full lifecycle governance on a single control plane. The page contains no news, incident, or release details.
Unit 42 research shows LLM safety refusals concentrate in a thin neural layer, motivating external, multi-layered AI security controls.
Palo Alto Networks Unit 42 introduces Perturbation Probing, a diagnostic technique for measuring the fragility of LLM safety mechanisms. The research finds that safety refusal behavior is localized within a thin neural layer, implying small perturbations can undermine built-in refusals. The authors argue this motivates external, multi-layered security defenses on top of model-internal safety training.
Unit 42's AI security assessment page describes Palo Alto Networks' offering for evaluating AI system security; no article text is available.
The item is a Palo Alto Networks Unit 42 product category page for 'AI security assessment' with no article body. It likely indexes Unit 42 content or services related to assessing the security of AI systems. No concrete incidents, vulnerabilities, or research findings are described.
Palo Alto Networks Unit 42 launched an OT Threat Research Lab to study how cyber threats disrupt industrial and critical-infrastructure operations.
Unit 42 announced a dedicated OT Threat Research Lab for testing emerging technologies, evaluating AI-OT scenarios, and examining how threats move across connected operational systems. The lab's findings are intended to inform vulnerability analysis, detection guidance, and risk prioritization for operational technology environments.
Palo Alto Networks Unit 42 published a threat bulletin, but no article body was available for detailed analysis.
The feed item contained only the title 'threat bulletin' with no article text. No specific incidents, actors, or vulnerabilities could be extracted from the available content.
Unit 42 warns attackers increasingly target CI/CD pipelines and developer tools rather than application code, urging full SDLC supply chain visibility.
Palo Alto Networks Unit 42 research argues attackers are shifting focus from application code to overlooked corners of the software development lifecycle supply chain, including CI/CD pipelines and developer tooling. The write-up calls for total SDLC visibility and strict security controls to defend these developer-facing attack surfaces.
Unit 42 archive page for Code to Cloud platform product-category articles; no new research or announcements.
This Unit 42 archive page lists product-category posts about Palo Alto Networks' Code to Cloud platform, which secures public cloud environments, SaaS applications and remote locations. It contains no new research, incidents, or announcements.
Unit 42 dissects FrostyGoop OT malware, which sent Modbus TCP commands to disrupt heating for 600+ apartment buildings in Ukraine.
Palo Alto Unit 42 analyzed new FrostyGoop/BUSTLEBERM samples, the ninth known ICS-centric malware, first used against a Ukrainian municipal energy company by sending Modbus TCP commands to ENCO control devices. The attack caused a two-day heating outage affecting over 600 apartment buildings during sub-zero temperatures. The Go-compiled malware accepts Modbus register operations and timing parameters via command-line arguments or a JSON configuration file, uses an obscure open-source Modbus library, and checks the Windows BeingDebugged flag to evade debuggers. Unit 42 telemetry recorded over 1 million Modbus TCP devices exposed to the internet between September and October 2024.
Unit 42 archive page for Prisma Cloud product-category articles; contains no new research or announcements.
This Unit 42 archive page lists product-category posts about Prisma Cloud, Palo Alto Networks' cloud security platform for public clouds, SaaS applications and remote users. It contains no new research, incidents, or product announcements.
Unit 42 details five vulnerabilities (CVSS 7.0-7.8) in ICONICS Suite SCADA software enabling privilege escalation and DoS.
Unit 42 discovered five vulnerabilities (CVE-2024-1182, CVE-2024-7587, CVE-2024-8299, CVE-2024-8300, CVE-2024-9852) in ICONICS Suite versions 10.97.2 and earlier for Windows during a 2024 security assessment. The flaws, rated CVSS 7.0-7.8, allow DLL hijacking, privilege escalation, information disclosure, denial-of-service and potentially full system compromise. ICONICS Suite is a SCADA solution with hundreds of thousands of installations in over 100 countries, widely used in critical infrastructure, and several dozen servers are internet-exposed per Unit 42 telemetry. ICONICS released patches and advisories with workarounds in 2024.
Unit 42 details CVE-2025-0921 (CVSS 6.5), a privileged file operations flaw in Iconics Suite enabling DoS and privilege escalation.
Unit 42 disclosed CVE-2025-0921 (CVSS 6.5), an execution-with-unnecessary-privileges flaw in the Pager Agent of the AlarmWorX64 MMX feature of Mitsubishi Electric Iconics Digital Solutions GENESIS64. Attackers could misuse privileged file system operations to corrupt critical binaries, causing denial-of-service or integrity loss on vulnerable SCADA systems. The analysis demonstrates a chain with CVE-2024-7587, which grants excessive permissions to the C:\ProgramData\ICONICS directory via the GenBroker32 installer. Iconics released an advisory with a workaround that addresses the reported issues.
Mirai-like botnet scans exploit TP-Link EOL router flaw CVE-2023-33538 after CISA KEV addition, though observed exploit code is flawed.
Unit 42 observed large-scale automated scans attempting to exploit CVE-2023-33538 in end-of-life TP-Link TL-WR940N, TL-WR740N and TL-WR841N routers after CISA added the flaw to its KEV catalog in June 2025. HTTP GET requests inject commands via the ssid1 parameter at the /userRpm/WlanNetworkRpm endpoint to download and execute an arm7 ELF binary, a Mirai variant related to the Condi IoT botnet. Firmware emulation and reverse engineering showed the observed exploits are flawed and would fail, but the underlying vulnerability is real and successful exploitation requires authentication to the router's web interface. TP-Link confirmed the devices are end-of-life with no patches available and recommends replacing units and eliminating default credentials.
Unit 42 uncovers TuxBot v3, an LLM-assisted IoT botnet framework with 17-architecture builds, Telnet brute-forcing, and DDoS capabilities.
Palo Alto Unit 42 identified TuxBot v3 Evolution, a modular IoT botnet framework derived from AISURU, Wuhan-lineage botnets, and MHDDoS. The C-based bot brute-forces Telnet with 1,496 credential pairs, targets over 30 IoT device families, and communicates with a Go-based C2 over encrypted TCP with multiple fallback mechanisms including DGA, P2P, and DNS TXT. LLM-assisted development left hallucinated crypto implementations and broken exploit modules in the analyzed samples, though roughly 70% of core functionality works. Researchers warn polished production builds likely exist, raising the threat potential.
Unit 42 identified MechaFlounder, a new Python backdoor used by the Chafer group to target a Turkish government entity via HTTP-based C2.
Unit 42 reports that in November 2018 the Chafer group targeted a Turkish government entity, reusing the win10-update[.]com infrastructure reported earlier in 2018 by ClearSky. The new secondary payload, tracked as MechaFlounder, is Python compiled with PyInstaller and acts as a backdoor supporting file upload/download and command execution. It beacons over HTTP using anomalous GET requests containing the username and hostname, suggesting a custom C2 server rather than a standard web server. Code overlap with OilRig's Clayside VBScript was noted, but Chafer and OilRig remain tracked as separate groups.
Aggah campaign abuses Bit.ly, BlogSpot, and Pastebin as multi-hop C2 to deliver RevengeRAT across the Middle East, US, Europe, and Asia.
Unit 42 details the Aggah campaign, which began with spearphishing emails in March 2019 spoofing a large financial institution and targeting education, media/marketing, and government organizations in the Middle East, later expanding to the US, Europe, and Asia. Delivery documents use Template Injection to load a remote OLE file whose macro runs mshta against a Bit.ly link redirecting to a BlogSpot post, which then uses Pastebin pastes to download RevengeRAT configured with a duckdns[.]org C2 domain. The embedded script also deletes Microsoft Defender signatures and kills Defender and Office processes, and modifies registry keys to enable macros. High-level TTPs resemble the Gorgon Group, but Unit 42 could not confirm attribution.
xHunt deployed custom backdoors Hisoka, Killua, Gon, and EYE against Kuwait shipping and transportation organizations, using DNS tunneling and email-draft C2 channels.
Unit 42 observed the xHunt campaign targeting Kuwait transportation and shipping organizations between May and June 2019, installing backdoors named after characters from the anime Hunter x Hunter, including Hisoka, Killua, Sakabota, and Netero. The tools use HTTP and DNS tunneling for C2, and Hisoka v0.9 added an unusual email-based C2 channel that abuses Exchange Web Services with stolen credentials to exchange email drafts. The Gon tool provides port scanning, file transfer, screenshots, remote command execution, and RDP session creation, while EYE acts as a cleanup failsafe if a legitimate user logs in. Related activity targeting Kuwait between July and December 2018 was reported by IBM X-Force IRIS, suggesting the campaigns are likely related.
xHunt's new CASHY200 PowerShell backdoor used DNS tunneling for C2; DNS security blocked beaconing at a Kuwait organization in September 2019.
Unit 42 describes CASHY200, a new PowerShell-based backdoor tied to the xHunt campaign that communicates with its C2 server windows64x[.]com via DNS tunneling, parsing commands from DNS answers. In September 2019, a Kuwait-based organization's DNS security service blocked the beaconing minutes after detection, cutting off adversary access. Samples dating to May and June 2018 and open source collection suggest the tool was used against Kuwait government organizations from spring 2018 through 2019, with malicious Word documents observed installing CASHY200 payloads configured with C2 domains such as firewallsupports[.]com and winx64-microsoft[.]com. The tool's name derives from identifiers stored under the registry key HKCU\Software\Microsoft\Cashe and the command value 200.
Unit 42 analyzed 67 million malicious URLs and domains in H2 2022, a 52% increase, highlighting phishing impersonation and web skimmer trends.
Unit 42 observed more than 67 million unique malicious URLs, domains and IPs between July and December 2022, a 52% increase over the first half of the year. Malicious JavaScript detections grew 99.3%, with over 4 million malicious JS samples hosted on 4.8 million URLs. Over 85% of hosting infrastructure was concentrated in eight countries, led by the United States, Brazil and China. The report also analyzes industries spoofed in phishing pages and includes a web skimmer case study on a Tranco top 1 million website.
Unit 42 details the Bad Likert Judge multi-turn jailbreak that abuses LLMs' evaluation capability, raising attack success rates over 60% across six frontier models.
Palo Alto Networks Unit 42 describes the Bad Likert Judge technique, a multi-turn jailbreak that asks a target LLM to act as a Likert-scale judge scoring the harmfulness of example responses. The highest-rated example in each scale can carry harmful content, bypassing the model's internal guardrails. Testing across six state-of-the-art text-generation LLMs showed an average attack success rate increase of more than 60% versus plain attack prompts, with tested models anonymized. The technique targets edge cases rather than typical use, and the article positions the work as guidance for defenders on potential jailbreak risks.
Unit 42 demonstrates nine framework-agnostic attack scenarios against AI agents built with CrewAI and AutoGen, causing data leakage, credential theft and remote code execution.
Palo Alto Networks Unit 42 investigated how attackers can target agentic applications, implementing two functionally identical apps with the open-source CrewAI and AutoGen frameworks and executing the same attacks on both. Nine attack scenarios produce outcomes including information leakage, credential theft, tool exploitation and remote code execution. Findings show most vulnerabilities are framework-agnostic, arising from insecure design patterns, misconfigurations and unsafe tool integrations rather than flaws in the frameworks themselves. The team published defense strategies per scenario and open-sourced the source code and datasets on GitHub.
Unit 42 PoC shows indirect prompt injection can poison Amazon Bedrock Agent long-term memory, enabling silent exfiltration of conversation history across future sessions.
Palo Alto Networks Unit 42 published a proof of concept showing that indirect prompt injection can silently poison the long-term memory of Amazon Bedrock Agents when the memory feature is enabled. Malicious content on a webpage or document manipulates the agent's session summarization process, so injected instructions persist across sessions and are added to later orchestration prompts, silently exfiltrating user conversation history. The issue is not a vulnerability in the Amazon Bedrock platform but an illustration of the broader unsolved LLM prompt-injection challenge. Amazon reviewed the research and stated that Bedrock Guardrails with the prompt-attack policy provides effective mitigation.
Unit 42 unveils agent session smuggling, where a rogue AI agent hides covert instructions in established Agent2Agent (A2A) protocol sessions to manipulate victim agents.
Palo Alto Networks Unit 42 discovered agent session smuggling, a new attack technique in which a malicious AI agent exploits an established cross-agent session under the Agent2Agent (A2A) protocol to send covert instructions hidden among benign client requests and server responses. The technique leverages the implicit trust agents place in collaborating agents and the stateful, multi-turn nature of A2A sessions; the researchers stress it affects any stateful protocol, not an A2A flaw. Unlike one-shot data-based attacks, a rogue agent can converse, adapt and build false trust over multiple interactions. Proposed mitigations include human-in-the-loop enforcement, cryptographically signed AgentCards for remote agent verification, and context-grounding to detect injected instructions.
Incorrect Default Permissions vulnerability in GenBroker32, which is included in the installers for Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mi
Incorrect Default Permissions vulnerability in GenBroker32, which is included in the installers for Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric GENESIS32 versions 9.70.300.23 and prior, Mitsubishi Electric Iconics Digital Solutions GENESIS32 versions 9.70.300.23 and prior, and Mitsubishi Electric MC Works64 all versions allows a…
Command Injection in TP-Link TL-WR940N, TL-WR841N, TL-WR740N Routers
CVE-2023-33538 is a command injection flaw (CWE-77) in the /userRpm/WlanNetworkRpm component of the web management interface on several legacy TP-Link routers. An attacker who can reach the router's management interface and send crafted requests to that component can cause the device to execute arbitrary operating-system commands. Successful exploitation typically gives the attacker full control of the router, enabling traffic manipulation, DNS hijacking, or pivoting into the connected network. Only the named hardware revisions are affected — TL-WR940N V2/V4, TL-WR841N V8/V10, and TL-WR740N V1/V2 — and these products may be end-of-life or end-of-service, meaning fixes may be limited or unavailable. The vulnerability was added to CISA's Known Exploited Vulnerabilities catalog on 2025-06-16, indicating exploitation in the wild, and its high EPSS score (41.9%, 99th percentile) points to substantial near-term exploitation risk.
Uncontrolled Search Path Element vulnerability in Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 a
Uncontrolled Search Path Element vulnerability in Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Hyper Historian versions 10.97.3 and prior, Mitsubishi Electric GENESIS32 all versions, Mitsubishi Electric MC Works64 all versions, Mitsubishi Electric Iconics Digital Solutions GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions Hyper Historian versions 10.97.3 and prior, and Mitsubishi…
Uncontrolled Search Path Element vulnerability in Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 a
Uncontrolled Search Path Element vulnerability in Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Hyper Historian versions 10.97.3 and prior, Mitsubishi Electric GENESIS32 all versions, Mitsubishi Electric MC Works64 all versions, Mitsubishi Electric Iconics Digital Solutions GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions Hyper Historian versions 10.97.3 and prior, and Mitsubishi…
Uncontrolled Search Path Element vulnerability in Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 a
Uncontrolled Search Path Element vulnerability in Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Hyper Historian versions 10.97.3 and prior, Mitsubishi Electric MC Works64 all versions, Mitsubishi Electric GENESIS32 versions 9.7 and prior, Mitsubishi Electric Iconics Digital Solutions GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions Hyper Historian versions 10.97.3 and prior, and…
Dead Code vulnerability in Mitsubishi Electric GENESIS64 Version 10.97.2, 10.97.2 CFR1, 10.97.2 CRF2 and 10.97.3, Mitsubishi Electric Iconics Digital Solutions
Dead Code vulnerability in Mitsubishi Electric GENESIS64 Version 10.97.2, 10.97.2 CFR1, 10.97.2 CRF2 and 10.97.3, Mitsubishi Electric Iconics Digital Solutions GENESIS64 Version 10.97.2, 10.97.2 CFR1, 10.97.2 CRF2 and 10.97.3, Mitsubishi Electric ICONICS Suite Version 10.97.2, 10.97.2 CFR1, 10.97.2 CRF2 and 10.97.3, and Mitsubishi Electric Iconics Digital Solutions ICONICS Suite Version 10.97.2, 10.97.2 CFR1, 10.97.2 CRF2 and 10.97.3 allows a local authenticated attacker to execute a malicious code by tampering with a specially crafted DLL. This could lead to disclose, tamper with, destroy,…
Execution with Unnecessary Privileges vulnerability in multiple services of Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONIC
Execution with Unnecessary Privileges vulnerability in multiple services of Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric MobileHMI versions 10.97.3 and prior, Mitsubishi Electric Hyper Historian versions 10.97.3 and prior, Mitsubishi Electric AnalytiX versions 10.97.3 and prior, Mitsubishi Electric IoTWorX version 10.95, Mitsubishi Electric GENESIS32 all versions, Mitsubishi Electric BizViz all versions, Mitsubishi Electric MC Works64 all versions, Mitsubishi Electric GENESIS versions 11.00,…
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