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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.
Attackers actively exploit CVE-2025-32433, a CVSS 10.0 unauthenticated RCE in Erlang/OTP SSH, heavily targeting OT and critical infrastructure networks.
CVE-2025-32433 enables unauthenticated RCE in Erlang/OTP's SSH daemon via SSH connection protocol messages (codes >= 80) processed before authentication, affecting versions before OTP-27.3.3, OTP-26.2.5.11 and OTP-25.3.2.20. Unit 42 recorded a spike in exploitation between May 1-9, 2025, with 70% of detections on firewalls protecting OT networks, disproportionately affecting healthcare, agriculture, media and high technology. Observed payloads bind TCP-connected shells or launch Bash reverse shells to hosts like 146.103.40.203:6667, with randomized DNS lookups under dns.outbound.watchtowr.com indicating OAST-driven blind RCE validation.
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 found a BOLA flaw, CVE-2024-22278 (CVSS 6.4), letting Maintainers improperly alter Harbor project metadata; fixed in versions 2.9.5, 2.10.3, and 2.11.0.
Unit 42 researchers identified a broken object-level authorization flaw, CVE-2024-22278, in Harbor, a CNCF-graduated cloud-native container registry with 1.8 million downloads. The flaw (CVSS 6.4) lets users with the Maintainer role create, update, and delete project metadata, actions reserved for ProjectAdmin, risking data exposure, integrity compromise, and circumvention of vulnerability scanning. Harbor patched the issue in versions 2.9.5, 2.10.3, and 2.11.0. The finding came from Unit 42's automated BOLA detection tool built on generative AI.
Unit 42's AI-powered tool found 15 BOLA vulnerabilities in Easy!Appointments, rated up to CVSS 9.9, letting low-privileged users escalate privileges; fixed in 1.5.0.
Unit 42's automated BOLA detection tool, built on generative AI, uncovered 15 broken object-level authorization flaws in the open-source scheduling application Easy!Appointments, tracked as CVE-2023-3285 through CVE-2023-3290 and CVE-2023-38047 through CVE-2023-38055. Nine flaws scored CVSS 9.9, letting logged-in customers view or manipulate appointments and accounts of providers and admins, including creating admin users for privilege escalation. The maintainers patched all issues in version 1.5.0. The same tool previously found a BOLA in Grafana (CVE-2024-1313).
Unit 42's BOLABuster methodology uses LLMs to automate detection of broken object-level authorization vulnerabilities, uncovering flaws in Grafana, Harbor, and Easy!Appointments.
Palo Alto Unit 42 details BOLABuster, a methodology combining large language models with heuristics to automate detection of broken object-level authorization (BOLA) flaws, which traditional fuzzing and static analysis struggle to find. The approach uses LLM reasoning to understand application logic, map endpoint dependency relationships, and generate and interpret test cases. It found CVE-2024-1313 in Grafana, CVE-2024-22278 in Harbor, and 15 CVEs in Easy!Appointments. The team is continuing to hunt for BOLAs in open-source and internal projects.
Unit 42's machine learning pipeline detected 6,729 DNS hijacking events between March and September 2024, hitting political parties, ISPs, and universities.
Unit 42 processes roughly 167 million new DNS records daily and applies a machine learning model using 74 features over 169 TB of passive DNS and geolocation data to flag hijacked domains. From March to September 2024 the pipeline screened over 29 billion records and classified 6,729 as DNS hijacking, averaging 38 detections per day; a new model detects hijacks in customer traffic within about 10 minutes. Notable cases include a Hungarian political party's hijacked domain, defacement of a large utility company and ISP, and university and research center domains repurposed for illicit gambling. DNS hijacking typically relies on stolen registrar or DNS provider credentials or cache poisoning, enabling MitM attacks, phishing, drive-by downloads, and scams.
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 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.
Unit 42 demonstrates that root access on a Kubernetes node lets attackers spoof SPIFFE/SPIRE attestation and harvest co-located workloads' SVIDs.
Palo Alto Networks Unit 42 describes post-exploitation techniques in which an attacker with root on a compromised Kubernetes node spoofs Linux cgroup metadata used by the SPIRE agent during workload attestation, tricking it into issuing a co-located workload's SPIFFE Verifiable Identity Document to an attacker-controlled process. The research shows the core trust assumption of machine-identity systems—that the node is trusted—collapses once root is obtained, exposing all cryptographic identities scoped to that node. Unit 42 released an open-source tool, Spooffe, for defenders to test identity exposure, and notes the technique has not been observed exploited in the wild.
Unit 42 exposed CL-CRI-1171, a pay-per-install operation using YouTube gaming channels and SEO poisoning to deliver RATs and a Chrome hijacker.
Palo Alto Networks Unit 42 identified over 10,000 OfferLoader samples and 11 YouTube channels pushing trojanized installers via gaming lures and SEO-poisoned searches for tools like WinDirStat and Bluetooth drivers. Trojanized Inno Setup installers spawn three payload branches: Insomnia RAT (dual Node.js/Python backdoor that disables Microsoft Defender), ARKTunnel (undocumented RAT hiding its payload in a bitmap image and tunneling TCP/UDP), and Docro Hijacker (alters Chrome protected preferences to hijack search and affiliate links). The pay-per-install model sells each infection to multiple buyers, reaching gamers as well as corporate and government endpoints.
Unit 42 analysis of 4 million malware reports finds 45% of C2-active samples connect directly to hard-coded IPs, bypassing DNS defenses.
Palo Alto Unit 42 analyzed over 4 million Advanced WildFire dynamic analysis reports and found that 45.32% of malware samples with C2 activity made at least one direct-to-IP connection, accounting for 23.17% of all C2 connection attempts. The firm proposes zero trust IP (ZT-IP), an enforcement approach that verifies whether outbound destinations were ever sanctioned by a DNS response. ZT-IP analysis surfaced Phorpiex ransomware droppers fetching payloads directly from C2 IPs, a persistent data exfiltration campaign using an obfuscated \GET protocol, and Mozi P2P botnet payloads delivered to IoT devices without DNS. Only 1% of benign samples connected directly to untrusted IP addresses.
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 clusters behavior of 40,000+ AWS identities from 125 cloud environments to map functional roles and enable lightweight SQL-based detection.
Palo Alto Unit 42 built an unsupervised behavioral clustering model using UMAP and HDBSCAN on AWS CloudTrail logs to map cloud identities to functional roles such as administrators, backup services, security tooling and DevOps. The study analyzed over 40,000 identities across 125 cloud environments over two months. The researchers show that heuristics extracted from the clustering map can be implemented in standard SQL, enabling role classification at scale without running a continuous ML pipeline. The methodology extends to audit logs from other cloud providers, SaaS and Kubernetes.
Unit 42 says Latin American attackers used LLMs to automate post-exploitation in campaigns hitting Mexican government, water utilities, and Brazilian financial firms.
Unit 42 identified two campaigns in Latin America whose operators used commercial LLMs (Claude, GPT-4.1) behind a self-hosted NextChat interface to generate and debug post-exploitation scripts. Cluster CL-CRI-1131 compromised a transportation organization, Mexican federal ministries, and water utilities in Mexico and Ecuador, using native Windows tools and Volume Shadow Copies to dump the SAM registry hive and NTDS.dit. Cluster CL-CRI-1163 targeted Brazilian financial organizations with job-themed phishing, custom RATs, and a Go-based reverse SOCKS5 tunneling utility called SockTz, with nine versions deployed within roughly two hours. Trend Micro tracks related AI-augmented activity as SHADOW-AETHER-040 and SHADOW-AETHER-064.