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Trends in Web Threats: Attackers Were More Active During Holiday Season
Unit 42 tracked 533,000 malicious landing URL incidents from October-December 2021, showing web threats peaked during the holiday shopping season.
Unit 42 detected 533,452 malicious landing URL incidents (120,753 unique) and 2,906,875 malicious host URL incidents (165,255 unique) from October through December 2021. Threat activity peaked in November, likely tied to Black Friday in the United States, United Kingdom, and Germany. Most malicious domains appeared to originate in the United States, followed by Russia and Germany. Personal sites, blogs, business sites, and shopping sites were the most common apparently benign entry points for attacks.
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Trends in Web Threats: Old Web Skimmer Still Active Today
Unit 42 detected 577,000 landing URL incidents in Q1 2022, with an old web skimmer family still actively stealing payment card data.
Palo Alto Unit 42 detected 577,275 landing URL incidents (116,643 unique) and 2,043,862 malicious host URL incidents (180,370 unique) between January and March 2022. Web threat volumes declined after the November 2021 holiday peak, but an old web skimmer family remained active. Business and economy sites overtook personal sites as the most common apparently benign entry points. Most malicious domains geolocated to the United States, Germany, and Russia, though proxy servers and VPNs obscure true locations.
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Trends in Web Threats in CY Q2 2022: Malicious JavaScript Downloaders Are Evolving
Unit 42 detected 751,000 landing URL incidents in Q2 2022 and documented malicious JavaScript downloaders evolving to evade detection.
Unit 42 detected 751,331 landing URL incidents (253,644 unique) and 1,744,629 malicious host URL incidents (256,844 unique) from April through June 2022. Total landing URL incidents rose compared with Q1 2022, and unique host URL incidents grew 42%, indicating attackers deploying more variants. The report includes a case study of a JavaScript downloader campaign demonstrating new evasion techniques. Personal sites, blogs, and business sites were the top apparently benign entry points.
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Autoencoder Is All You Need: Profiling and Detecting Malicious DNS Traffic
Palo Alto Unit 42 details an autoencoder-based method that profiles DNS traffic to detect C2 and malicious domains, blocking ~374,000 malicious DNS requests daily.
Unit 42 built an RNN-based autoencoder that compresses DNS traffic time series into fixed-dimensional 'DNS profiles' for each domain and device. Downstream classification, clustering, and anomaly detection modules flag suspicious domains, capturing 170 emerging suspicious domains in May 2024. Signatures block roughly 374,000 malicious DNS requests daily and run in the Advanced DNS Security service, with detections shared to Advanced URL Filtering. Case studies link DNS traffic patterns to C2 beaconing, dynamic DNS abuse, and DNS tunneling for data exfiltration.
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TLD Tracker: Exploring Newly Released Top
Unit 42 tracked 19 newly released top-level domains and found large-scale phishing, unwanted program distribution, and cybersquatting tied to TLD launch dates.
Researchers analyzed 19 new generic TLDs, including .zip, .bot, .ing, and .meme, released or approaching general availability over roughly 18 months. Data from passive DNS, registry zone files, newly registered domain feeds, and the Tranco top-1M list showed phishing campaigns, potentially unwanted program distribution, and domain squatting on these TLDs. Abuse correlated with each TLD's rollout phases, indicating attackers monitor general availability dates to register and weaponize domains. The IANA root database now lists over 1,000 generic TLDs.
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Beneath the Surface: Detecting and Blocking Hidden Malicious Traffic Distribution Systems
Unit 42 built an ML-based detector for malicious traffic distribution systems, finding malicious TDS chains average longer redirections and more URLs than legitimate ones.
Traffic distribution systems redirect victims through chains of intermediate domains to hide final destinations, serving phishing, malvertising, and online gambling operations. Unit 42's topological analysis of redirection graphs found malicious TDS traffic uses longer chains (about 25% exceed four hops vs 10% benign), more URLs (median 126 vs 80), and fewer isolated subgraphs with higher connectivity. These features power an ML detector integrated into Advanced DNS Security and Advanced URL Filtering to identify and block malicious TDS infrastructure in customer traffic.
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Almost Half of Malware Samples Communicate Direct to IP
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.
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Harnessing LLMs for Automating BOLA Detection
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.
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