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Search: “web crawling”

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CategoryAllVulnerability 31Data breach 28Ransomware 25Malware 23Exploit / PoC 14Industry 13Phishing & fraud 13Research 12Policy & legal 12Threat actor 10AI safety & security 5AI tools & infra 4Tools 4AI industry 1
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Automatically Detecting DNS Hijacking in Passive DNS

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.

Palo Alto Unit 42 · Aug 17, 2026Research in the wild

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.

Palo Alto Unit 42 · Aug 17, 2026Research

You Get What You Sample: Evaluating Sampling Strategies for Web Security Measurements

Evaluation of 500k Tranco and 24.8M Common Crawl hosts shows Top-N domain sampling biases web security measurements; probability sampling yields unbiased estimates.

The study is the first comprehensive investigation of how sampling strategies affect web security measurement conclusions, comparing datasets and strategies across 500k Tranco domains and 24.8M Common Crawl hosts. It shows Top-N selection does not reflect the overall web distribution and may bias observed vulnerability rates, while probability-based strategies yield stable, unbiased prevalence and impact estimates. Hybrid sampling offers no advantage because its deterministic prefix consistently hurts accuracy, and the authors propose an adaptive probability-based strategy effective even when target prevalence is unknown.

arXiv cs.CR · 7d agoResearch1

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.

Palo Alto Unit 42 · Aug 17, 2026Research

terms.txt: A Consent and Compensation Protocol for Agentic Web Access

terms.txt specifies a robots.txt-style protocol for per-path, per-purpose AI crawler consent and compensation, with enforcement adding 0.20-0.65 ms per request.

The paper documents that automated clients now make up most web requests, that training dominates Cloudflare-classified crawling, and that the largest AI platforms fetch thousands of pages per returned visitor while robots.txt cannot express identity, purpose, terms, or price. It specifies terms.txt plus an origin-enforced exchange using Web Bot Auth signatures, signed intent, delegation tokens, HTTP 402 negotiation, and signed receipts. A dependency-free implementation adds 0.20 to 0.65 ms per request on one vCPU.

arXiv cs.CR · 7d agoResearch1

CDN Tsunami Attack Abuses HTTP/3 Translation for Up to 350x DoS Amplification

Researchers disclosed two DoS attacks exploiting CDNs' HTTP/3-to-HTTP/1.1 translation to amplify low-bandwidth requests up to 350x against origin servers.

Researchers named the two techniques HTTP/3 Bandwidth Amplification (HBA) and HTTP/3 Connection Amplification (HCA), exploiting the gap where CDNs speak HTTP/3 to browsers but only HTTP/1.1 to origins. All six tested CDNs (Alibaba, Baidu, Cloudflare, CloudFront, Fastly, Tencent) were susceptible to HBA, and five to HCA, with Cloudflare immune to HCA because it buffers full requests before opening backend connections. Attackers using under 5 Mbps forced over 100 Mbps at origins, and the 350x factor applies only to Alibaba, Baidu, and Tencent, which support the QPACK dynamic table. Testing identified 42,330 potentially vulnerable subdomains out of 151,685 hosted on the six providers across the Tranco Top 1M.

The Hacker News · 28d agoResearch

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.

Palo Alto Unit 42 · Aug 17, 2026Research