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Recent Trends in Internet Threats: Common Industries Impersonated in Phishing Attacks, Web Skimmer Analysis and More
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.
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Keys to the Kingdom: Erlang/OTP SSH Vulnerability Analysis and Exploits Observed in the Wild
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.
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CVE-2022-22965: Spring Core Remote Code Execution Vulnerability Exploited In the Wild (SpringShell) (Updated)
Attackers actively exploit Spring Framework RCE CVE-2022-22965 (SpringShell, CVSS 9.8) to deploy webshells; patches 5.3.18/5.2.20 shipped March 31, 2022.
CVE-2022-22965 enables unauthenticated remote code execution in the widely used Spring Framework (CVSS 9.8), which Unit 42 has observed being exploited in the wild. The flaw stems from getCachedIntrospectionResults exposing the class object during parameter binding, letting attackers manipulate the class loader to modify Tomcat logging and upload a JSP webshell. Public PoCs require JDK 9+, Tomcat, WAR packaging, and spring-webmvc or spring-webflux dependencies on Spring 5.3.0-5.3.17, 5.2.0-5.2.19, or older. Fixes shipped in Spring Framework 5.3.18 and 5.2.20; the related Spring Cloud Function flaw CVE-2022-22963 was patched March 29, 2022.
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Introducing Unit 42’s Attribution Framework
Unit 42 releases its Attribution Framework, a systematic method using Diamond Model and Admiralty scores to attribute activity clusters to named threat actors.
Palo Alto Networks' Unit 42 introduced a structured framework for threat actor attribution built on the Diamond Model of Intrusion Analysis and Admiralty reliability/credibility scoring. The framework tracks activity at three levels: activity clusters (named CL-STA, CL-CRI, CL-UNK, or CL-MIX), temporary threat groups, and named threat actors using the constellation naming schema. Analysts score evidence across TTPs, tooling, malware code, OPSEC, infrastructure, timelines, and victimology to decide when to merge or elevate clusters, avoiding premature group naming.
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Threat Advisory: Critical Apache Log4j vulnerability being exploited in the wild
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GTIG AI Threat Tracker: From Prompting to Autonomy – The Evolution of Adversarial AI
GTIG's Q2 2026 tracker shows adversaries adopting agentic AI workflows, including credential harvesting in under six hours and supply chain attacks by UNC6780.
Google Threat Intelligence Group's Q2 2026 report documents adversaries moving from basic prompting to agentic AI workflows and automation, including a cloud compromise followed by agent-enabled mass credential harvesting executed in under six hours. It tracks financially motivated actor UNC6780 (TeamPCP) conducting large-scale open source supply chain compromises across PyPI, npm, and Docker Hub since March 2026, deploying credential stealers. The report also highlights growing targeting of proprietary AI models, source code, prompts, and API credentials, plus LLMJacking practices where adversaries steal developer credentials or hijack cloud infrastructure to run unauthorized AI workloads.
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