Cyber security

Binance, the world's largest crypto exchange, has linked the theft of about $6.3 billion in cryptocurrency to a DevOps engineer's compromised laptop. [...]

Cybersecurity researchers have disclosed that the official Anthropic Claude Code extension was compromised on the Open VSX registry to install a persistent SSH backdoor on infected hosts. The issue has since been traced to the compromise of the Cursor Marketplace, prompting the AI coding platform Cursor to briefly take down the service to remove the malware. "We identified a security incident

Binance freezes funny money after compromise of engineer’s laptop linked to $6.3 billion of unauthorized trades

Cryptocurrency giant Binance said on Thursday that a "security incident" on its platform has impacted about $6.3 billion worth of assets. The world's biggest cryptocurrency exchange said nearly $3 billion of those assets have been frozen on-chain so far. Tether and Circle said they have frozen more than 1.567 billion USDT and 800 million related to the incident, respectively. The

Cryptocurrency exchange Binance is investigating a potential security breach involving up to $5.8 billion in digital assets, in what could become the largest cryptocurrency hack in history. [...]

A flaw in the Linux kernel's TCP code, tracked as CVE-2026-98432, affects Ubuntu, Red Hat Enterprise Linux (RHEL), and other Linux distributions. The kernel's own CVE team rates it 9.8 out of 10 on the CVSS scale. Anthropic said on October 27 that its AI model wrote an exploit that uses the flaw to let a remote attacker with no credentials run commands as root on Linux servers with a

Cisco released security updates to patch a denial-of-service zero-day in Adaptive Security Appliance (ASA) and Threat Defense (FTD) software that attackers are exploiting to remotely crash vulnerable firewall devices. [...]

Drupal has warned that threat actors are exploiting a "highly critical" security flaw in its Commerce Core component. The vulnerability, tracked as CVE-2026-118539 (CVSS score: N/A), is a case of missing authorization affecting Commerce Core versions from 0.0.0 to 2.41 and from 3.0.0 to 3.3.13. "This module enables you to sell goods and services to customers through an included shopping cart

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Thursday added two security flaws impacting Cisco and GitLab software to its Known Exploited Vulnerabilities (KEV), citing evidence of active exploitation. The vulnerabilities are listed below - CVE-2026-97229 (CVSS score: 10.0) - An integer overflow or wraparound vulnerability in GitLab Community Edition and Enterprise

arXiv:2610.37738v1 Announce Type: new Abstract: Modern agent frameworks allow users to provide Markdown files containing system prompts, tool descriptions, and skill definitions (e.g., SKILL.md), which are incorporated into the agent's context during initialization. In this work, we investigate whether this configuration mechanism can also serve as an injection channel: can a jailbreak embedded in a Markdown skill file propagate through an agent and induce prohibited behavior? To answer this question, we construct a LangChain-based question-answering agent whose system prompt is assembled from a user-provided Markdown file. We embed a jailbreak combining persona manipulation with a GPTFUZZER-based attack and evaluate its effectiveness across seven contemporary open-weight models using 270 forbidden questions spanning nine harm categories. Under a benign system prompt, the models exhibit near-complete refusal behavior, with a mean attack success rate (ASR) of just 0.5%. In contrast, the injected skill file raises the mean ASR to 45%, reaching 68.9% for individual models. We further evaluate two payload obfuscation strategies: an invisible zero-width character-based stealth payload a…

arXiv:2610.37741v1 Announce Type: new Abstract: Large language models are pretrained on vast, uncurated corpora scraped from the public web. This creates a security risk from attackers who plant poisoned content in the pretraining data. We show for the first time that such poisoning can plant a remote code execution backdoor in an agent, causing it to execute attacker-chosen commands on the user's machine. To measure the threat end to end, we pretrain Qwen3 models from scratch at sizes 0.6B, 1.7B, and 4B, and then post-train each into a CLI agent using a clean pipeline. Poisoning only 0.1% of the pretraining corpus with our new poison design implants the backdoor: the deployed agent executes the malicious command when the trigger is present while behaving normally on clean inputs. The attack success rate (ASR) increases with model scale, with the 4B model reaching roughly 92% ASR at a 0.1% poison rate and staying positive even when the poisoning rate drops to 0.0005%. Our code is available at https://github.com/safety-research/agentic-backdoor.

arXiv:2610.38110v1 Announce Type: new Abstract: Deep neural networks (DNNs) are widely deployed in safety- and privacy-critical domains such as healthcare and finance, where models are trained on confidential data. This paper presents the first hardware-level attribute-stealing attack that bypasses state-of-the-art privacy defenses, revealing that accelerator optimizations intended for performance can directly undermine data confidentiality. SparseBleed requires no access to gradients, logits, model weights, ground-truth labels, power traces, DVFS manipulation, shared caches, or other channels. Instead, we show that sparsity-driven execution paths encode stable timing signatures that act as a surrogate for model confidence, enabling high-fidelity attribute inference from a single query. SparseBleed generalizes across heterogeneous backends and hardware engines, including Intel AVX2/AVX-512/AMX, NVIDIA Ampere Tensor Cores with 2:4 sparsity, and nine well-known sparse and dense libraries such as Intel oneDNN, oneMKL SparseBLAS, and NVIDIA cuSPARSE. We evaluate SparseBleed to infer sensitive input attributes and true output labels. On Texas-100X, SparseBleed infers sensitive input att…

Prediction market platform Polymarket temporarily suspended trading on Thursday after hackers compromised a third-party service to distribute cryptocurrency-draining JavaScript, with approximately $2.5 million in assets traced to a suspected attacker wallet. [...]

Article URL: https://arkm.com/explorer/entity/21a94755-f604-45e5-9501-6542e23005e8 Comments URL: https://news.ycombinator.com/item?id=50296470 Points: 536 # Comments: 298

Some attacks take real skill. Others just need someone to leave a door open. This week has both. Exposed passwords, old bugs still getting exploited, and trusted software doing things it shouldn't. There's a strange mix of clever tricks and careless mistakes. Hackers are borrowing trust from familiar apps, websites, and AI tools. Some are using AI to speed things up. Others are leaving their

Snoops 'likely still' have access to US government-affiliated network

A newly identified advanced persistent threat (APT) group tracked as UAT-11473 was observed exploiting CVE-2026-20127, an authentication-bypass flaw in Cisco Catalyst SD-WAN Controllers, to deploy a custom malware framework called KeyDrift. [...]

A massive search-hijacking campaign involving 319 Chrome extensions with a combined 13.4 million reported installations redirected users' searches through tracking servers to generate advertising revenue from Bing and Yahoo. [...]

Officials confirm this week’s tax deadlines extended to November as a result

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) warned federal agencies to patch a high-severity Linux kernel privilege escalation bug that attackers are chaining with the PHP2Shell flaws in the wild. [...]

Article URL: https://text.npr.org/nx-s1-6021497 Comments URL: https://news.ycombinator.com/item?id=50287898 Points: 135 # Comments: 24

View CSAF Summary Hitachi Energy is aware of OS command injection and Dirty Frag vulnerabilities that affect CoreTec product versions listed in this document. Successful exploitation of these vulnerabilities could result in impact on confidentiality, integrity and availability of the product. Please refer to the Recommended Immediate Actions for information about the mitigation/remediation. The following versions of Hitachi Energy CoreTec Product are affected: CoreTec5 vers:CoreTec5/<=3.4.0, 5.0.0 (CVE-2026-13162, CVE-2026-43284) CoreTec4 vers:CoreTec4/<=3.4.0 (CVE-2026-13162) CVSS Vendor Equipment v3 8.8 Hitachi Energy CoreTec5 2 Vulnerabilities Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection'), Write-what-where Condition Background Critical Infrastructure Sectors: Energy Countries/Areas Deployed: Worldwide Company Headquarters Location: Switzerland Vulnerabilities Expand All + CVE-2026-13162 An OS command vulnerability exists in CoreTec product that allows authenticated users with certain roles to execute arbitrary system commands with full control over the device. The vulnerability exists in the configuration daemon's time synchronization…

View CSAF Summary Mendix SAML module contains an authentication bypass vulnerability that could allow an unauthenticated remote attacker to gain unauthorized access by replaying a valid SAML assertion in IdP-initiated authentication flows. Siemens has released new versions for the affected products and recommends to update to the latest versions. The following versions of Siemens Mendix SAML Module are affected: Mendix SAML (Mendix 10 compatible) vers:intdot/<4.2.4 (CVE-2026-89209) Mendix SAML (Mendix 11 compatible) vers:intdot/<4.2.4 (CVE-2026-89209) Mendix SAML (Mendix 9 compatible) vers:intdot/<3.6.29 (CVE-2026-89209) CVSS Vendor Equipment v3 6.8 Siemens Mendix SAML (Mendix 10 compatible) 1 Vulnerability Authentication Bypass by Capture-replay Background Critical Infrastructure Sectors: Critical Manufacturing, Information Technology Countries/Areas Deployed: Worldwide Company Headquarters Location: Germany Vulnerabilities CVE-2026-89209 Affected versions of the module do not properly protect against SAML assertion replay in IdP-initiated authentication flows. This could allow unauthenticated remote attackers to gain unauthorized access to the application by replaying a valid SAM…

View CSAF Summary Hitachi Energy is aware of multiple vulnerabilities that affect the TRO600 series product versions listed in this document. These vulnerabilities can be exploited for remote code execution (RCE) to carry out further attacks and disclosure of sensitive information. Please refer to the Recommended Immediate Actions section for information about the mitigation/remediation. The following versions of Hitachi Energy TRO600 series products are affected: TRO600 vers:TRO600/<=9.2.1.6, vers:TRO600/<=9.1.1.0, 9.2.2.1, 9.1.2.0 (CVE-2026-10756, CVE-2026-106087) CVSS Vendor Equipment v3 7.5 Hitachi Energy TRO600 2 Vulnerabilities Improper Restriction of Communication Channel to Intended Endpoints, Missing Authentication for Critical Function Background Critical Infrastructure Sectors: Energy Countries/Areas Deployed: Worldwide Company Headquarters Location: Switzerland Vulnerabilities Expand All + CVE-2026-10756 TRO600 contains a vulnerability that allows an authenticated attacker to bypass network access restrictions and access services intended to be reachable only from localhost. By leveraging port forwarding, an attacker can interact with locally bound ports and services, r…

CISA has added one new vulnerability to its Known Exploited Vulnerabilities (KEV) Catalog, based on evidence of active exploitation. CVE-2026-97229 GitLab Community Edition and Enterprise Edition Integer Overflow or Wraparound vulnerability This type of vulnerability is a frequent attack vector for malicious cyber actors and poses significant risks to the federal enterprise. Binding Operational Directive (BOD) 26-04: Prioritizing Security Updates Based on Risk establishes vulnerability management requirements for Federal Civilian Executive Branch (FCEB) agencies. BOD 26-04 reinforces the importance of the KEV Catalog and requires federal agencies to prioritize rapid remediation of high-risk vulnerabilities, specifically those identified by Common Vulnerabilities and Exposures (CVEs) listed in CISA’s KEV Catalog on publicly exposed assets that grant total control of the asset post-exploitation, while deferring action for lower-risk vulnerabilities. BOD 26-04 further establishes basic expectations for when agencies must check whether threat actors compromised the system before the patch was applied. While BOD 26-04 applies only to FCEB agencies, CISA encourages all organizations to a…

Customer says they received fraudulent charges on their card hours before company notified them

arXiv:2610.37872v1 Announce Type: new Abstract: Public exploit code for a newly disclosed vulnerability usually appears before any published rule that detects its exploitation. Of the 45 CVEs we evaluate, 37 ever received a Sigma, Elastic or Splunk rule; in 33 the exploit came first, by a median of 22 days. Analysts still write every rule by hand. We present Encore, which compiles a public Python proof-of-concept exploit (PoC) into CVE-specific hunting rules over a web service's existing logs, without running it. Encore rests on the PoC Behavior Alignment Principle: a PoC performs the exploit, so its code already provides a rule's HTTP requests, payloads and host operations. That is hard to act on: a PoC is written against libraries and runs on the attacker's machine, while the target's web-server, proxy and host logs record only what arrived and what it did. Encore closes the gap with a map from library calls to the operations they cause, built once per library, keeping only the operations visible on the target. For 12 of the 45, Encore's rule states the exploit's own payload fragment or host operation, which is evidence of the exploit itself, and not of traffic to its endpoint. O…

arXiv:2610.37901v1 Announce Type: new Abstract: An agent skill packages natural-language instructions, scripts, and reference files that an LLM-based agent loads for a task. Skills may access the file system, shell, and network under user-authorized permissions; weaknesses can lead to unsafe execution. Existing scanners primarily target malicious skills, not unintended vulnerabilities in public repositories, and analyze skills in isolation, overlooking multi-skill interactions. To address these gaps, we developed a six-module scanner: Content Extraction, Local Analysis, Composite Analysis, Validation, Reporting, and Mitigation. Local Analysis detects unintended vulnerabilities in benign skills. On a 556-case leaderboard subset of SkillTrustBench, it achieved 95.90% recall with a 4.22% false positive rate; on a balanced sample of 1,000 MalSkillBench skills, its F1 was 0.947, outperforming Cisco's Skill Scanner, NVIDIA's SkillSpector, AgentSkillsScanner, and SkillSieve. In 1,587 skills from the 75 most-starred GitHub repositories, three analysts confirmed 53 of 59 findings (89.8% precision) across 23 repositories. We developed proof-of-concept (PoC) attacks for each vulnerability, di…

arXiv:2610.38209v1 Announce Type: new Abstract: LLM-based agents now share experiences on social platforms and remember new preferences to enhance their performance. However, existing memory systems contain a vulnerability in role-to-principal attribution that attackers can exploit. We identify Role Grounding Misattribution, in which an attacker-designed interaction causes the victim agent to incorrectly record a preference from the attacker as a preference of the victim's user. Specifically, we are the first to formalize Role Grounding Attack (RGA), an attack model that exploits this vulnerability through nested conversation structures. In the memory formation stage, an experience-sharing post quoting a conversation between the attacker agent and its user is published, which explicitly identifies its publisher. The inner conversation expresses preferences from the user of the attacker agent. When the victim agent remembers preferences from this nested structure, its memory loses the context binding and grounds the attacker's preference to the victim's user instead. In the memory retrieval stage, the misattributed preference persists and influences the victim agent's decisions when…

arXiv:2610.38524v1 Announce Type: new Abstract: Large language models (LLMs) have enabled more effective vulnerability scanning of web applications. However, these capabilities also make it significantly easier for attackers to automate cyberattacks on the web. In this work, we investigate how to divert malicious LLM-based web scanning, with preservation of genuine functionality as a deployment requirement. We systematically study six LLM-based scanners and summarize how they gather information and iteratively select actions based on observations. Guided by the study, we then present WebMaze, a defense that preserves genuine application functionality while diverting LLM-based scanners to a generated decoy world. WebMaze embeds functionality-coupled traps in genuine pages and constructs view-tailored interactive decoy worlds from the application's existing views. The traps blend into genuine components and expose entry points to decoy worlds that sustain exploration through connected pages and interactions without changing genuine functionality. Across 12 web applications and three LLM-based scanners under the default no-disclosure setting, decoy pages account for an average of 93.0…

arXiv:2610.38617v1 Announce Type: new Abstract: We ask what AI coding agents and AI security harnesses find that deterministic static application security testing (SAST) does not, and what they miss that SAST catches. VulnBench 2.0 is a benchmark of 108 hand-curated, attacker-reachable vulnerabilities in 20 open-source web applications in Go, JavaScript/TypeScript, Python and Java; 62 flow across more than one file. A finding is credited only if it locates the vulnerability's attacker-controlled source and its dangerous sink, each within two lines. We evaluate nine productized configurations under one blind protocol: the Snyk Code static analyzer, Claude Code's /security-review with three model and effort settings (one with the Snyk MCP server), Codex Security with GPT-5.6 Luna, Terra and Sol, and the DeepSec scanner with two model backends. Snyk Code reached 82.4% macro recall (89 of 108) and Claude Code with Opus 5 at xhigh effort 79.3% (85 of 108); they disagree on 28 vulnerabilities, split 16 to 12, too few to tell them apart. Neither engine was sufficient alone: together they reached 101 of 108, the ceiling of all seven comparable configurations combined, and the AI configurat…

arXiv:2610.38799v1 Announce Type: new Abstract: Coding agents increasingly develop, test, and integrate software whose components are owned by different developers, teams, or services. This exposes a distinct attack surface: a malicious or compromised peer development role can manipulate a live component of the software under development (e.g., a backend service, test harness, CI job, or deployment service) and influence the runtime feedback that another role's coding agent must process to complete its task. Although such a component has legitimate diagnostic authority to report its own state, it has no action authority over the victim's local files, credentials, shell, or development environment. We introduce Cross-Boundary Adversarial Runtime Injection (CARI), a direct runtime-feedback injection setting that exploits this authority mismatch. Rather than explicitly compromising the victim's prompt, model, tools, or local environment, the attacker covertly embeds malicious instructions in task-relevant runtime feedback produced by a peer-controlled development component. The victim then interprets that feedback as part of normal debugging or recovery, and may translate the diagnost…

arXiv:2610.39122v1 Announce Type: new Abstract: Self-evolving personal agents act for users and learn from user feedback. However, if an agent obeys an instruction that an untrusted third party hides in content, the third party gains control of the agent. Such indirect prompt injection (IPI) poses severe risks. We investigate whether self-evolution degrades the robustness of personal agents to IPI. To study this question, we design a controlled two-phase methodology. In the adaptation phase, the user asks the agent to read external content and complete the task described in it, a common request in daily use. The agent then learns from the user's feedback and improves itself as designed. In the test phase, a third party writes a similar task into new external content, but the user's test request does not authorize the injected task. Using this methodology, we build multiple tasks with diverse test instances in different domains for a real personal agent. Our extensive experiments reveal that self-evolution substantially degrades robustness to IPI. Specifically, compared with the base agent, which does not evolve from user feedback, the average attack success rate rises by 14.0--48.8…

arXiv:2610.39351v1 Announce Type: new Abstract: Classifying malware binaries rendered as images is a popular approach to malware family identification, and papers on the standard benchmarks for this task report accuracies of 97-99%. In this paper we audit ten of these benchmarks for near-duplicate images and test-train leakage, and retrain standard classifiers under naive and content-aware splits. Deduplicating by file hash, which is the standard practice, does not prevent leakage, because distinct binaries render to identical images. Deduplicating on image content reduces top-1 accuracy by up to 8 points and macro-F1 by up to 15 points on the released benchmarks we find leaky. The rendering step also adds duplication, since byteplots of the same binaries contain 19.7% near-duplicates where static feature vectors contain 2.2%, and changing the width at which bytes are wrapped into rows reduces accuracy by 20 to 33 points, although no byte changes. We recommend that content-level duplication and a non-image baseline be reported alongside accuracy.

arXiv:2610.39509v1 Announce Type: new Abstract: Large language models (LLMs) deployed in safety-critical settings must resist adversarial inputs designed to elicit harmful outputs. We present a systematic empirical evaluation of five adversarial jailbreaking attack families---direct requests, roleplay-based jailbreaking, hypothetical framing, instruction override, and obfuscation---across seven open-weight and API-accessible LLMs: Llama-3-8B, Llama-3.1-8B, Llama-3.2-3B, Mistral-7B, Qwen2.5-7B, Gemma2-9B, and Phi-4-14B. We further evaluate four defense mechanisms: keyword blacklisting, self-reminder prompting, paraphrase-based sanitization, and perplexity-based filtering against the two most vulnerable models. Our results reveal that obfuscation attacks achieve a mean attack success rate (ASR) of 54.3% across all models and constitute the most transferable adversarial vector, bypassing every evaluated model with nonzero success. We find that model size does not predict adversarial robustness (Spearman r=-0.57, p=0.18), and that models within the same architecture family exhibit dramatically different vulnerability profiles. Self-reminder prompting achieves the most consistent defens…

arXiv:2610.39758v1 Announce Type: new Abstract: Indirect prompt injection threatens local LLM agents through untrusted tool outputs, motivating defenses that suppress attacks while preserving task evidence under limited inference budgets. We introduce SEAL, which learns a soft prompt and scoring readout offline to sanitize tool outputs using a frozen task model. SEAL constructs scoring queries from trusted initial context and the learned prompt, keeping the reference independent of inspected tool text. It scores tokens without normalization across context tokens and uses preservation regularization to encourage retention of useful evidence during localized deletion. This design requires neither defense-generated tokens nor a separate verifier model. Across AgentDojo and AgentDyn, SEAL suppresses attacks while improving task completion under attack. Specifically, on AgentDojo, SEAL reduces attack success from 34.6\% to 0.1\% and increases Joint---task completion without attack success---from 51.4\% to 77.5\%, while using approximately $127\times$ fewer defense-related tokens than PISanitizer, a state-of-the-art prompt injection defense. Replication on a second backbone with the same…

arXiv:2610.39910v1 Announce Type: new Abstract: Deterministic variants of CSIDH remove randomness from the group action, either through implementation techniques or, as in Houben's deterministic CSIDH and OSIDH-LD, by design. Since earlier fault attacks on CSIDH mostly target random point sampling, determinism seems to close that avenue. We show that it opens another: every secret-dependent step occurs at a fixed, public point of every execution, so an attacker can fault the same step repeatedly. Our attack forces the mask that selects which of two lanes computes the next isogeny to zero, for example by skipping one instruction. Whether the output changes reveals whether a secret exponent reaches a chosen threshold, and a binary search over these thresholds recovers the full key with one fault per execution: at most 297 oracle queries for OSIDH-LD and 222 for deterministic CSIDH with CSIDH-512-sized keys. We recover full OSIDH-LD keys in a software fault simulation of the reference implementation, and show on a ChipWhisperer-Lite that the targeted instruction is skipped with probability at least 0.94 at an isolated fault position. Finally, we argue that fault resistance requires gi…

arXiv:2610.40396v1 Announce Type: new Abstract: Despite growing capabilities, LLMs remain susceptible to prompt injections: malicious inputs that can manipulate or hijack models. In particular, it has recently been observed that LLMs can exhibit role confusion (Ye et al., 2026), where models attribute the source of text based on its style instead of its labeled role. Malicious text in tool outputs (for instance, a malicious webpage accessed by an agent) can leverage role confusion to impersonate the user, an attack we call role spoofing}. In this work, we seek to understand and mitigate role spoofing. We study how models track "who said what" in a conversation, showing that they use cues from both content (e.g., semantics and writing style) and structure (e.g., control tokens and formatting). We show that LLMs are highly susceptible to role spoofing attacks that combine both fake structure and content. Additionally, in a toy setting, we show that adversarial training which teaches the model to ignore content-based injections does mitigate content-based attacks but leaves the model susceptible to structure-based attacks. This can explain why frontier models, with increased resistanc…

Article URL: https://www.penligent.ai/hackinglabs/cve-2026-98432/ Comments URL: https://news.ycombinator.com/item?id=50299182 Points: 38 # Comments: 28

Note: The dark mode of this site is kind of hard to read. Comments

Japan Airlines (JAL) has announced that a data breach at Amadeus's Passenger Service System exposed the data of about 92,000 customers. [...]

A fake cryptocurrency trading app called Padre Terminal, previously available on the Apple App Store for iOS devices, has been linked to the theft of approximately $23,000 from 27 Solana wallets. [...]

Threat actors are delivering malicious loaders and cryptocurrency miners under the guise of artificial intelligence (AI) desktop applications for Anthropic Claude and OpenAI ChatGPT as part of a campaign dubbed Yoba. The activity involves creating fake GitHub projects that claim to be desktop applications like GPT-5.6 Sol, GPT-6 Astra, and Claude Opus, only to deliver a malware family called

Security updates have been issued by AlmaLinux (cjose, emacs, expat, gcc-toolset-15-gcc, java-17-openjdk, java-25-openjdk, kernel, kernel-rt, libX11, libXi, libXrender, libXtst, mod_auth_openidc:2.3, pki-deps:10.6, thunderbird, webkit2gtk3, and yggdrasil), Debian (chromium, firefox-esr, and openjdk-25), Fedora (firefox, librabbitmq, libreswan, python-pillow, and unbound), Mageia (fio and python3, python-pip), Oracle (buildah, cjose, container-tools:ol8, emacs, flute, libbase, libfonts, libformula, liblayout, libloader, libreoffice, librepository, libserializer, pentaho-libxml, pentaho-reporting-flow-engine, and sac, kernel, libX11, libXi, libXrender, libXtst, podman, python-urllib3, thunderbird, and yggdrasil), Red Hat (firefox, goose, kernel, kernel package, kernel-rt, librsvg2, multiple packages, php:7.4, sudo, thunderbird, and xorg-x11-server), and SUSE (alsa, ca-certificates-mozilla, chromedriver, cifs-utils, coredns, coreutils, cpupower, curl, cyrus-sasl, docker, dpdk, haproxy, kernel, libpoppler-cpp3, libsndfile-devel, libzbar-devel, mozilla-nss, MozillaFirefox, nginx, openai-codex, and perl-Cpanel-JSON-XS).

Cloudflare mitigated over 8,200 DDoS attacks per hour in Q3 2026 as attackers shifted to shorter, high-frequency strikes. Explore the latest threat data, including the collapse of CLDAP vectors and the impact of recent botnet takedowns.