Cyber security

A large-scale watering hole attack associated with Russian hackers has been targeting iOS and Android users visiting compromised sites or fake ones impersonating legitimate portals. [...]

A critical Android flaw could allow an attacker to run code on a phone without any user action remotely, Google said in its November security bulletin. The flaw, CVE-2026-58868, affects Android 14 through 17. Phones get the fix with security patch level 2026-11-01 or later, which phone makers and carriers send on their own schedules. As of November 4, Google's bulletin did not report

The largest crypto theft to date was pulled off by combining some well-known hacking techniques over the course of a few months, according to Binance. A postmortem from the company says the process started in June with one developer downloading a tampered installer apparently spread by a malicious Google ad, exposing details about the engineers and how they work. Then, in September, another engineer was targeted by someone pretending to be interested in hiring them; that person set up a series of calls and multiple rounds of interviews, shared social media pages to build trust, and eventually prompted them to download a fake interview assign … Read the full story at The Verge.

Cryptocurrency exchange Binance says North Korean hackers used a fake job interview to trick an employee into installing malware that ultimately let them compromise the company's wallet infrastructure and steal about $6.34 billion in digital assets. [...]

Binance on Tuesday said the record-setting theft of $6.43 billion in assets from the world's largest cryptocurrency exchange was the culmination of a "sophisticated, multi-stage campaign" that involved the use of a tampered Homebrew installation and recruitment-themed social engineering attack targeting two of its engineers. "It was not a single isolated breach or a simple phishing incident,"

We opened a ticket with support about Gitbook availability issues and got this in reply. We have yet to see any official customer communication or breach notification. Apologies for the formatting, did the best I could as it's a mobile paste. Update: GitBook security incident update Hey , The GitBook app, the GitBook API, and published docs are available again. A few services may still be degraded as we finish restoring everything to full performance. *What happened* GitBook has been the target of a sophisticated, highly targeted attack using AI agents. At 19:00 CET, November 2, the attacker gained unauthorized access to some of our infrastructure credentials. We detected the attack immediately and acted swiftly to block further impact. To make sure our infrastructure was fully secure, we then took the GitBook app and authenticated docs sites offline. We kept publicly published content live for as long as possible, using cached data. *What we did immediately* * Blocked the attacker and closed the vulnerabilities involved * Replaced every credential and secret across our infrastructure * Brought services back only once we were confident they were secure * Invalidated every customer’…

Fixed kernels for a use-after-free vulnerability in the Linux kernel's IPv4 TCP code are now available for Red Hat Enterprise Linux 8, 9, and 10, while Ubuntu's fixes for its three newest long-term-support releases remain in its opt-in testing channel with no date for a regular update. The flaw, tracked as CVE-2026-98432, can be reached by a remote, unauthenticated attacker and can

arXiv:2611.04291v1 Announce Type: new Abstract: Modern browsers are complex software platforms that users can customize through browser extensions to further expand their functionality. Extensions derive their functionality from privileged browser APIs that expose powerful capabilities. Prior work has extensively studied how malicious webpages or extensions can target those capabilities. However, prior research has overlooked the NativeMessaging API, which allows extensions to create a communication channel that bridges the gap between web content rendered in the browser and native apps running on the user's machine. This API enables workflows that cross the browser's boundary to perform critical operations (e.g., digital signing) or exchange sensitive data (e.g., credentials). In this paper, we conduct the first, to our knowledge, empirical security analysis of NativeMessaging, and present a novel attack that allows a non-privileged malicious native app to misuse browsers' native messaging capabilities to stealthily intercept sensitive communications or launch confused deputy attacks against other native apps. To demonstrate the significant risk introduced by native messaging, we …

Attackers have compromised the update infrastructure for the cPGuard web security suite to distribute a backdoor and a data-stealing script. [...]

Developer platform WorkOS suffered a data breach after a threat actor accessed business contact records and summaries of customer communications. [...]

Blockchain infrastructure provider Chainstack on Tuesday disclosed that it was targeted in a cyber attack that allowed an unauthorized party to gain access to some parts of its infrastructure. The incident is assessed to be part of a large-scale, artificial intelligence (AI)-powered attack campaign that came to light last week. "An external attacker gained unauthorized access to part of

Google has cut ties with the company behind the botnet campaign.

UEFI bootkit BlackX exploits Microsoft's lack of clarity around Secure Boot protections.

Google on Monday warned that threat actors are hijacking enterprise access to Large Language Models (LLMs) to fraudulently leverage expensive computing resources and AI services. The tactic is called LLMjacking, a form of resource hijacking adapted for the LLM era, where an attacker obtains an API key through phishing, source code leaks, misconfigured environments, or exposed client-side code

Cybersecurity company Barracuda Networks warned customers to patch two vulnerabilities that can let attackers run commands with root privileges. [...]

A 3AM ransomware affiliate targeted a school estate in the UK and successfully encrypted around 37 of nearly 50 machines reached from an unmanaged host. [...]

CISA has added one new vulnerability to its Known Exploited Vulnerabilities (KEV) Catalog, based on evidence of active exploitation. CVE-2026-119977 WordPress Core Time-of-check Time-of-use (TOCTOU) Race Condition 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 adopt risk-bas…

Russian authorities have arrested three suspected members of a threat group known as VasyGrek and dismantled its criminal infrastructure. Also known as Fluffy Wolf, the threat actor has a history of targeting Russian and international companies since 2016. The Ministry of Internal Affairs of the Russian Federation (MVD) said members of the group are "suspected of embezzling funds from the

Comments

arXiv:2611.03742v1 Announce Type: new Abstract: Static application security testing (SAST) tools are often deployed on the assumption that detectors configured today will remain effective on future code, including previously unknown vulnerabilities. We test this assumption using 20,000 vulnerable and fixed C/C++ cases from 1,116 open-source projects (2016-2025), evaluated with three CodeQL releases, three Infer releases, and one Semgrep configuration. To distinguish temporal effects from shifts in code composition, we compare yearly populations, project- and label-matched year pairs, and within-project trajectories. We investigate the largest within-project declines through repair diffs, detection predicates, alert locations, and execution logs, while accounting for failed, degraded, and costly analyses. Under changed-file case-label scoring, overall F1 ranges from 0.066 to 0.478, but count-based AUROC stays near chance (0.495-0.526). Newer CodeQL releases attain higher F1 alongside nearly parallel rises in recall and false-positive rate, not stronger separation of vulnerable from fixed cases. In project-matched year pairs, CodeQL R2024 has negative mean later-minus-earlier F1 at e…

arXiv:2611.03752v1 Announce Type: new Abstract: Third-party LoRA adapters introduce serious backdoor risks: malicious adapters can maintain normal task performance while embedding hidden, trigger-activated behaviors, making them difficult to detect through standard utility evaluations. Existing removal-based defenses typically sanitize backdoored models by updating, pruning, distilling, or mixing model parameters. However, these approaches are misaligned with adapter-based deployment, where the trusted base model is expected to remain frozen. In this work, we propose LoRA-Patch, a lightweight backdoor defense tailored to LoRA adapters. Our key insight is that backdoor injection distorts the directional geometry of a poisoned adapter relative to its trusted base model. Building on this insight, LoRA-Patch suppresses trigger-activated behaviors by realigning adapter directions through layer-wise steering, without modifying the base model weights. Extensive experiments across five backdoor attacks and three models show that LoRA-Patch effectively mitigates trigger-activated backdoors, outperforming existing baselines while preserving benign task performance. Moreover, LoRA-Patch is co…

arXiv:2611.03754v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promise for empowering search agents to tackle complex information-seeking tasks. However, since acquiring external information necessitates web search, these agents are highly susceptible to various adversarial attacks (e.g., malicious information injection or stealthy context manipulation). Existing attack strategies inevitably rely on static and human-crafted templates, failing to emulate the dynamic and complex nature of real-world threats. In this paper, we propose an adversarial self-evolution framework that integrates bi-level optimization between the attacker's strategy evolution and the defender's adaptive mitigation, to expose the vulnerabilities of search agents. Specifically, we introduce the Contrastive Rollout Evolutionary Optimization (CREO) method to drive the directional evolution of the attack strategy population via contrastive evolutionary signals. To provide a stable environment for this continuous evolution, we further construct a controlled sandbox and design fine-grained metrics to quantify internal vulnerabilities. Extensive experiments across various multi-hop QA benchm…

arXiv:2611.03759v1 Announce Type: new Abstract: Restricting what a model can say is often mistaken for restricting an attacker's influence over its decision. An attacker can add text to the evidence and steer the model toward a wrong answer that still belongs to the allowed set. We evaluate seven decision models spanning hosted APIs, open-weight models and an encoder on 16 tasks in seven domains from Decision Bench, targeting every wrong option across 240 text examples (990 targets per model). We present Slot Attack, a black-box attack designed for decision models that appends text assembled from named components to steer predictions toward an attacker-selected incorrect option. The method relies solely on returned label probabilities, achieves success rates of 32--99\% on initially correct decisions, and identifies the effective component combinations with a median of 17--72 queries per successful attack. Accuracy and confidence do not reliably reveal this vulnerability. Models with near-identical accuracy on the sampled tasks differ by about 20\%age points in attack success. We demonstrate that an attacker can force not just one wrong answer but every available one: on about 20\%…

arXiv:2611.03772v1 Announce Type: new Abstract: Third-party Agent Skills extend what an autonomous agent can do, but a malicious Skill can hide data exposure or output manipulation inside a legitimate workflow. Neither reading a Skill's description nor granting it tool permissions tells whether its execution meets the user's security requirements. We introduce RuntimeTickets, a protocol that enforces and audits these requirements while a Skill runs. From released malicious-Skill datasets we derive a risk model and build adversarial-benign Skill pairs. From the task's delivery contract, a language model proposes security obligations for that task. Each obligation cites the Skill lines it answers, and the ticket tightens the host's approvals into fine-grained allowed actions but never loosens them. A runtime gate checks the recipient, object and amount of every outbound send before forwarding it, and trusted monitors record the effects within their observation scope. The ticket links these checks and observations to the approved obligations of each Skill run, and a deterministic decoder turns the ticket's status into governable data flows and verifies each obligation. Neither enforce…

arXiv:2611.03854v1 Announce Type: new Abstract: Securing bootloaders is critical to establishing the trusted computing base of modern systems. TianoCore's edk2, the de facto UEFI-based bootloader, is widely deployed but has recently been shown to contain numerous high-severity vulnerabilities. Existing approaches either focus on post-boot interfaces or rely on heavyweight simulation, limiting their scalability, throughput, and coverage of boot-time components. We present Metis, an automated technique for fuzzing edk2 protocols (i.e., boot-time interfaces) by rehosting them as user-space applications. Our key insight is that effective fuzzing must exercise not only protocol interfaces but also their dependency interactions, which serve as additional input channels. Metis employs a Direct-dependency Component Rehosting (DCR)-based strategy to construct semantically meaningful mocks for dependencies, enabling controlled input injection across all channels. It further leverages a hybrid approach combining static analysis and Large Language Model (LLM)-guided synthesis to generate and refine fuzzing harnesses. We implement Metis on the Host Based Firmware Analyzer (HBFA) framework and e…

arXiv:2611.04122v1 Announce Type: new Abstract: Speech watermarking is becoming a key defense for digital copyright and content authentication. Existing attacks treat evasion as a removal problem: they erase the watermark, relying on detector access or on speech data that is hard to collect, such as paired clean-watermarked or clean target speaker speech. Commercial platforms are moving to close off these assumptions. TrustedAudio, for instance, restricts detection to authenticated users and denies meaningful detection feedback to unauthorized parties. Adversaries thus cannot tell whether a sample is watermarked or which watermark it carries, nor reliably remove it without degrading speech quality. In this work, we show that evading a speech watermark does not require erasing it: detection relies on the structural consistency of the embedded pattern, and disrupting this consistency is enough to defeat it. Building on this observation, we identify harmonic structure as an effective attack surface, as perturbing it breaks watermark consistency while preserving perceptual quality. We therefore propose ScrambleMark, a zero-query, watermark-agnostic, black-box attack that evades detecti…

arXiv:2611.04393v1 Announce Type: new Abstract: LLM agents increasingly act on information retrieved from public sources they treat as credible, which turns a manipulated recommendation into a transaction, a dependency, or a decision with physical consequences. The security literature models this threat as instruction injection, in which an adversary writes a command into retrieved content and deployed defenses learn to recognize command syntax. We show that an attacker can instead steer an agent through the agent's own harm-avoidance. A single fabricated hazard, written as an ordinary review, leads the agent to disqualify the legitimate options and select the attacker's target by its own reasoning, with no command for a detector to recognize. We call this voluntary servitude. These payloads steer agents about as often as instruction injection (60.4% vs. 66.0%), and deployed injection detectors score them as benign, so behind a filter they are 1.7-4.4x more effective. We measure this at the generation stage across six models and ten consumer-service domains, for three attack families, and show that it transfers to production browser agents, outperforming instruction injection, and …

arXiv:2611.04461v1 Announce Type: new Abstract: We evaluate, at implementation level and under an explicit threat model, the verification pipelines of three Verifiable Credential (VC) formats: SD-JWT VC; the W3C Verifiable Credentials Data Model 2.0 with the Data Integrity cryptosuite eddsa-rdfc-2022 (W3C VCDM); and mdoc (ISO/IEC 18013-5). For W3C VCDM we identify and exercise three structural attack surfaces from remote context resolution and RDFC-1.0 canonicalization: denial of service (DoS) via poison graphs, context injection through @context, and server-side request forgery (SSRF) reachable via malicious URLs, all at default settings. A poison graph, whose blank-node references are symmetric enough to force RDFC-1.0 into deep recursion, raises it to 278x the baseline from five blank nodes in 527 bytes, and to 13.5 s from eight. Every experiment ran in four environments sharing runtime and library versions; the multiple moves with the machine (251--424x for one input), not with its speed. Cost tracks that symmetry rather than the node count: an acyclic input with 1,500 blank nodes stays at 462--714x. A call limit separates the two: every attack family aborts at 16 or below, eve…

arXiv:2611.04672v1 Announce Type: new Abstract: Prompt injection is usually discussed and evaluated as a single, homogeneous type of attack. However, vulnerabilities reported in real-world systems show that prompt injections vary widely. Moreover, the same instruction can be a prompt injection in one context and a legitimate request in another, depending on the user's intent and the system's policies. Ignoring this variation leaves gaps in how defenses are evaluated, and can give a false sense of security against real-world attacks. We identify nine dimensions along which prompt injection attacks differ. Together, they form a taxonomy that describes the injected instruction itself, its relation to the user's task, and how it manifests in the target system. Each dimension affects which defenses can detect or prevent an attack. We first use the taxonomy to characterize six real-world prompt injection vulnerabilities, showing how much these attacks differ. We then apply it to nine prompt injection benchmarks and find that most cover only a small region of this space. Combining benchmarks helps, but 13% of pairwise category combinations remain untested, mostly among the less commonly r…

arXiv:2611.04673v1 Announce Type: new Abstract: Indirect Prompt Injection (IPI) poses a critical threat to LLM agents. Existing defenses largely focus on whether an injection induces a new tool invocation or unintended action. In this paper, we study a different attack surface, parameter poisoning, where the attacker preserves the intended action but manipulates one or more tool arguments, such as a recipient or other task-critical value. Such attacks are difficult to detect because no new action is introduced, while parameter changes themselves are often legitimate parts of the user's request. Our key insight is that the legitimacy of a parameter change should be determined by the trust of the information flow that produces it under the user query. The query induces a task-specific trust hierarchy over available information fields: some are trusted to determine particular parameters, while others should have little or no influence. Based on this insight, we propose AuthorityFlow, a three-stage parameter-level defense. First, it interprets the user query to construct a task-specific trust hierarchy over the information fields available to the agent. Second, for each tool argument, …

arXiv:2611.04717v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks by generating explicit Chain-of-Thought (CoT) reasoning, but the safety implications of this intermediate reasoning surface remain underexplored. We demonstrate that the reasoning trace itself is a powerful attack vector. Safety alignment in large models is substantially less robust to malicious reasoning embedded in the context than to direct imperative prompts. We introduce BrainwashedLLM, a weak-to-strong adversarial framework that automates the discovery of reasoning-based jailbreaks. First, we use Group Relative Policy Optimization (GRPO), a Reinforcement Learning (RL) algorithm, with a multi-objective rule-based reward to fine-tune a small, white-box source model to generate adversarial reasoning traces. Crucially, the reward is computed entirely over the source's own reasoning trace. Second, we show that these traces transfer in a zero-shot manner. When traces are injected as input prefixes, they steer larger black-box LRMs to continue the adversarial reasoning and produce harmful outputs, without requiring prompt optimization, gradient access, or any for…

arXiv:2611.04400v1 Announce Type: cross Abstract: Personalized neural audio watermarking systems embed user-specific payloads into audio to trace the user associated with a leaked or misused copy. Existing evaluations mainly assume access to a single personalized copy, but real-world distribution may expose multiple copies of the same recording. We find that simple sample-wise averaging breaks traceability: averaging only two copies produces tracing-failure rates of 87.7-99.7% across the five systems while largely preserving audio quality. Decoder outputs show distinct responses to coalition payload composition across systems, with high majority agreement in TimbreWM but substantially lower agreement in WMCodec. In a more sophisticated payload-aware setting, mixture weighting can further steer the decoder toward a selected nonmember, achieving a target success rate of 92.3% in TimbreWM. Preliminary confidence screening rejects many collusion-induced assignments while retaining most valid single-copy outputs. Code and results are available at https://github.com/Silwan5z/neural-audio-watermark-collusion-attack. This work highlights multi-copy traceability as an additional evaluation …

arXiv:2611.04701v1 Announce Type: cross Abstract: A zero-knowledge virtual machine (zkVM) lets an untrusted prover convince a verifier that a program, typically Rust compiled to RISC-V, ran correctly. The proof covers the compiled binary, so a miscompilation can make a correct zkVM produce a valid proof of a computation that differs from the developer's source. We present the first empirical study of miscompilation as a threat to zkVM guarantees, on the production zkVMs SP1 and Jolt. We assemble 88 known rustc and LLVM miscompilations with Rust reproducers, using an LLM agent to recover Rust triggers for 60 of 124 LLVM bugs reported only as LLVM IR. Many of the bugs carry over to zkVMs: 48 of the 88 make a guest commit a wrong output on SP1 and 46 on Jolt, including the same 15 rustc bugs on both. Most bugs that do not reproduce never reach the faulty code on the zkVM's target, e.g. because they need an instruction-set extension. Existing guest programs are rarely affected by accident. Across 13 guests of 10 popular SP1 projects, the buggy and the fixed compiler produce byte-identical binaries in 447 of the 552 guest-bug pairs that build, and the other 105 have the same code or agr…

arXiv:2601.20638v2 Announce Type: replace Abstract: Dependency management systems are a critical component in software development, enabling the incorporation of existing functionality. However, misconfigurations and malicious actors in these systems pose security risks, leading to supply chain attacks. Despite the widespread use of smartphone apps, the security of dependency management systems in the iOS software supply chain has received limited attention. In this paper, we focus on CocoaPods, one of the most widely used dependency management systems for iOS app development, but also examine the security of Carthage and Swift Package Manager (SwiftPM). We demonstrate that iOS apps expose internal dependency names and versions. Attackers can exploit this information to register previously unclaimed dependencies in CocoaPods, enabling remote code execution (RCE) on developer machines and build servers. Additionally, we show that attackers can compromise dependencies by reclaiming abandoned domains and GitHub URLs. Analyzing 9,491 real-world iOS applications, we find that a significant fraction is vulnerable to these attacks, putting millions of users at risk, including applications…

arXiv:2606.11817v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained Decoding (GCD) has been widely adopted to improve the reliability of LLM-generated code by enforcing syntactic validity. In this paper, we reveal a counterintuitive risk: this reliability-oriented technique can itself become an attack surface. We uncover a new jailbreak attack, termed CodeSpear, that exploits GCD to induce LLMs into generating malicious code. Our experiments show that simply applying a benign code grammar constraint can effectively jailbreak LLMs. To address this vulnerability, we propose CodeShield, a safety alignment approach that robustly preserves safe behavior even under attacker-controlled grammar constraints. CodeShield aligns the model in the code modality by teaching it to generate honeypot code under GCD. Such code is semantically harmless, so it does not implement the malicious request, and structurally diverse, so it is difficult to suppress through grammar tightening. At the same time, CodeShield still preserves natural-language ref…

arXiv:2606.15123v2 Announce Type: replace Abstract: We study the task of CVE-conditioned exploit generation, where a model drafts proof-of-concept (PoC) exploits given software vulnerability context. We adopt a data-centric approach, constructing a high-quality dataset via multi-stage preprocessing and introducing a scalable evaluation framework with LLM-as-judge and fine-grained rubrics. Under this unified setup, we benchmark 17 large language models across 8 evaluation criteria, providing systematic insights into their zero-shot capabilities. We further show that a compact 8B open-weight model, when fine-tuned on curated data, achieves over 42.5% improvement in exploit quality and rivals some proprietary models when combined with simple test-time rejection strategies. Our results highlight the importance of data quality, structured supervision, and evaluation design for reliable exploit generation, suggesting that these factors can be as critical as model scale in adapting LLMs to cybersecurity tasks.

arXiv:2610.35541v3 Announce Type: replace Abstract: Web services increasingly expose backend interfaces that let users delegate tasks to AI agents, which invoke service operations directly without navigating websites. This shift changes how existing Web vulnerabilities can be exploited. We investigate these changes through VELA, an executable benchmark covering 44 vulnerabilities across twelve Web application families. The benchmark preserves vulnerable backends and derives agent-facing interfaces from legitimate workflows. We evaluate three commercial agents across multiple interface configurations, using controlled comparisons to separate interface effects from agent behavior and independent oracles to verify attack outcomes. Our results show that the agent realizes every attack that direct interface invocation realizes in the reference configuration, so exploitability is set mainly by the interface: 91% of vulnerabilities remain exploitable under the reference design. Interface restrictions prevent some attacks by removing required operations or limiting input control, while retention remains high across vulnerability classes. No case-level loss in the reference configuration is…

Customers of the mSecure password manager are being urged to act now to protect the confidential information they store in it, possibly by changing passwords, resetting two-factor authentication (2FA), and removing passkeys. The warning came in an email sent on November 3 in the name of developer mSeven Software. It says the company recently learned of suspicious activity linked to the app but

Security updates have been issued by AlmaLinux (bind, bind9.16, firefox, freerdp, ghostscript, kernel, kernel-rt, libXi, perl-YAML:1.24, perl:5.32, pipewire, qt5-qtbase, redis:6, tomcat, and wireshark), Debian (chromium, git-annex, incus, kernel, and strongswan), Fedora (barman, erlang-hex_core, glib-networking, kernel, knot, liblerc, libsoup3, mingw-cfitsio, openjpeg, perl-libnet, pipx, python-pillow, python-uv-build, radicale, ruff, rust-archive-trait, rust-astral-ini, rust-astral-mail-headers, rust-astral-pubgrub, rust-astral-version-ranges, rust-astral_async_zip, rust-quick-junit, rust-tar-codec, rust-tar-framing, tpm2-tss, ty, uv, and zvbi), Mageia (unbound), Red Hat (buildah, compat-openssl10, compat-openssl11, container-tools:rhel8, delve, golang-github-openprinting-ipp-usb, grafana, grafana-pcp, kernel, oci-seccomp-bpf-hook, openssl, opentelemetry-collector, osbuild-composer, podman, rhc, and skopeo), Slackware (expat), SUSE (ansible-core, apache-arrow, apache-commons-codec, apache-commons-io, apache-commons- logging, avalon-framework, avalon-logkit, glassfish-jsp, jsch, maven-file- management, opentest4j, aws-nitro-enclaves-cli, bouncycastle, chromedriver, chromium, cifs-u…

Zero Wire on Cloudflare Radar pairs plain-language briefings with global attack telemetry and remediation steps. Triage critical CVEs faster and decide how to protect your infrastructure.

Comments

European law enforcement authorities arrested seven suspects and seized more than 100 servers in a joint action targeting an unnamed company suspected of providing fraudulent trading websites to criminal gangs. [...]

Cyber agency warns critical infrastructure is a likely target as chief urges bosses to stop doing 'tech on the cheap'

In both a 2021 essay and my 2022 book, A Hacker’s Mind, I talk about AIs finding vulnerabilities and exploits—hacking—the legal system. Here’s some demonstrations of this from Germany: Legal systems encode the foundations of social order in natural language. Similar to computer systems, the written law can contain vulnerabilities that invite exploitation.1 Courts absorb most exploits through interpretation, and such vulnerabilities rarely surface in the first place. With highly capable AI systems enabling the discovery of vulnerabilities at unprecedented scale, we believe the latter could change. In testing this hypothesis, we have identified a substantial number of vulnerabilities in the German legal system using LLMs. To our knowledge, none of these have been previously recorded. We share a selection of vulnerabilities below, and present potential exploits for each. We share only those vulnerabilities whose exploitation we believe would not cause serious harm. We believe that with cheap legal reasoning proliferating, AI systems could jeopardize the functioning of today’s legal systems. In such a world, lawmakers would need to significantly harden the process of legislation, for e…

View CSAF Summary Successful exploitation of these vulnerabilities could allow an attacker to execute unauthorized SQL commands and gain access to unauthorized resources. The following versions of Subnet Solutions PowerSYSTEM Center are affected: PowerSYSTEM Center 2020 =6.0.x| =7.0.x| =6.0.x| =7.0.x| =6.0.x| =7.0.x| =6.0.x| =7.0.x|<=7.1.x Product Status: known_affected Remediations Mitigation Subnet Solutions recommends users update to PowerSYSTEM Center 2026 Update 1.1. For assistance in upgrading, users should contact the Subnet Solutions System Integration team or customer support team at (403) 270-8885 or by email at support@subnet.com. If unable to update to the latest version, Subnet Solutions recommends users disable user access to the report designer. Additional Metrics Relevant CWE: CWE-89 Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection') CVSS Version Base Score Base Severity Vector String 3.1 9 CRITICAL CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H 4.0 9.4 CRITICAL CVSS:4.0/AV:A/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H View CVE Details Acknowledgments Chinmay Patel of Digital Boundary Group reported these vulnerabilities to CISA…