GPUThor is a new Rowhammer attack technique targeting Nvidia Ampere GPUs with GDDR6 memory, significantly increasing the effectiveness of bit flips in video memory. It exploits a hardware behavior where repeated memory access can corrupt data in neighboring cells, bypassing standard defenses like Target Row Refresh (TRR). The attack can cause double and triple-bit errors, which are not fully corrected by ECC memory, leading to denial of service conditions such as GPU reboots and hardware faults. While arbitrary code execution has not been demonstrated, the attack shows a theoretical potential to compromise industrial GPUs and cloud infrastructure using such accelerators.
CVE-2026-85786 is a memory-amplification denial of service vulnerability in the Amazon ion-java library. It arises from an incomplete fix for a previous vulnerability (CVE-2026-75936) related to GZIP auto-decompression. The issue allows denial of service via highly compressed data expansion. Versions of ion-java prior to 1.12.1 are affected. The vulnerability has been addressed in ion-java version 1.12.1, and no workarounds are available.
GPUThor is a newly disclosed Rowhammer attack targeting NVIDIA Ampere-class GPUs with GDDR6 memory, including RTX A4000, A4500, A5000, and A6000. It bypasses NVIDIA's ECC protections by exploiting undocumented GPU behaviors and a non-uniform hammering pattern to induce high bit-flip rates. This enables denial-of-service conditions and root-level privilege escalation by corrupting GPU page tables. The attack was demonstrated to cause GPU resets and eventual hardware marking for replacement. NVIDIA recommends enabling SYS-ECC and IOMMU/DMA isolation, monitoring error telemetry, and restricting untrusted workloads. Complete protection likely requires future hardware-level defenses and stronger multi-bit ECC. No bit flips were observed on tested GDDR6X or HBM2e GPUs using the same attack patterns.
A large distributed denial-of-service (DDoS) attack has disrupted Norway's shared government digital infrastructure, impacting public sector services since Monday. The attack has caused significant service interruptions but no specific technical details or exploited vulnerabilities have been disclosed.
Other noteworthy stories that might have slipped under the radar: Threema DDoS attack, Evooo1Bot Linux botnet, Crypto4A secures top-tier NIST certification. The post In Other News: Zombie Card Attack, T-Mobile Cut Cable to Stop Hackers, GitHub Denies AI Caused Bug appeared first on SecurityWeek .
Multiple distributed denial-of-service (DDoS) attacks targeted the Threema secure messaging service earlier this week, causing severe disruptions to communications. [...]
Cisco has issued a warning about a high-severity denial-of-service (DoS) vulnerability affecting Secure Firewall ASA and Threat Defense (FTD) software. This flaw is actively exploited in the wild to remotely crash affected devices, disrupting their operation. The vulnerability impacts VPN functionality on these Cisco products. No specific affected versions or patch information is provided in the available data.
The security defects could be exploited for arbitrary code execution and denial-of-service. The post Adobe Urges Immediate Patching of Critical ColdFusion, Campaign Classic Flaws appeared first on SecurityWeek .
Cloudflare reported a significant surge in extremely large network-layer distributed denial-of-service (DDoS) attacks exceeding 1 Tbps during the second quarter of 2026, with over 800 such attacks mitigated, a more than fivefold increase from the previous quarter. The company also mitigated a record-breaking 31.4 Tbps attack attributed to the Aisuru/Kimwolf botnet. Overall DDoS activity increased substantially in both network-layer attacks and malicious HTTP requests. Despite the rise in large-scale attacks, most incidents remained small and brief. Attack techniques shifted toward DNS-related and reflection/amplification methods. The Media, Production, and Publishing sectors were the most targeted for HTTP DDoS requests, with the government sector also seeing increased attacks linked to geopolitical events. Cloudflare attributes a decline in activity after April to an international crackdown on DDoS-for-hire services.
Cisco has disclosed two high-severity vulnerabilities in the ClamAV scanning engine used by its Secure Endpoint Connector. These flaws allow unauthenticated remote attackers to crash the ClamAV scanning process by submitting specially crafted ZIP files, causing denial-of-service (DoS) conditions. The vulnerabilities affect ClamAV versions 1.5.0 through 1.5.3 and were patched in version 1.5.4. The impact is particularly significant on Windows platforms where ClamAV runs with privileged security context. Proof-of-concept exploit code is publicly available, but there is no evidence of active exploitation in the wild. Cisco plans to release updates for affected Secure Endpoint Connector versions across Windows, Linux, and Mac platforms. No workarounds exist for these vulnerabilities.
Discover how Kimwolf v7 targets Android IoT devices with HTTP/2 DDoS fingerprinting, Ethereum ENS C2 resolution and Tor backup routing. The post Kimwolf v7: An Evolution of the Kimwolf Botnet appeared first on Unit 42 .
Remote, unauthenticated attackers could exploit the bugs to cause a denial-of-service (DoS) condition. The post Cisco Warns of High-Severity ClamAV Vulnerabilities With Public PoC appeared first on SecurityWeek .
A heap-buffer-overflow vulnerability exists in the nothings stb TrueType library, up to version 1.26, that is used for parsing TrueType font files. The vulnerability exists in the stbtt__GetGlyphShapeTT() function within the nothings stb_truetype.h library when parsing malformed TTF (TrueType Font) files. The vulnerability resides in the glyph data parsing path.
An attacker can craft a malformed TTF file with an inflated endPtsOfContours value and truncate the remaining glyph data. When an application utilizing stb_truetype.h (such as various game engines or graphics software) attempts to load, bake, or render this malformed font via stbtt_GetGlyphShape(), the parser will attempt to read past the end of the glyph data buffer, triggering the out-of-bounds read.
Actor usage of AI is exploding. By analyzing artifacts left behind, Talos has created a detailed analysis of how we are seeing adversaries leverage the technology to include development, force multiplication, and vulnerability research. Based on the evidence Talos gathered, guardrails did not provide much protection, with most actors able to convince the models to comply despite the lack of sophisticated techniques or encoding. The pre-existing skill of the actor has a large impact on what they can accomplish with AI. Talos observed novice users able to create malicious capabilities, albeit with limited capabilities and success. Advanced users were able to build astonishing capabilities, pushing the models to create sophisticated and complex outputs. Artificial intelligence (AI) and associated language models are now ubiquitous and heavily used in both personal and professional contexts to streamline tasks and expand capabilities. With AI being used everywhere and by almost everyone, one of the biggest questions is how malicious actors are taking advantage. Fortunately, actors make mistakes and chatbots leave artifacts. Leveraging cloud-based AI models leaves behind a variety of artifacts, most notably a prompt log. These logs can take on a variety of shapes and sizes, but they are left on endpoints that are running various applications, such as Claude Code, CodeX, Cursor, or Gemini. Over the course of our research, we’ve collected a significant corpus of these files and can start discussing the ways we see bad actors leveraging these technologies. In conducting the research, three categories of activity emerged. One was using AI as a malicious software engineer, leveraging AI to write (in some cases) very sophisticated code with clear malicious intentions. Another was actors leveraging AI to scale criminal operations and campaigns. Finally, there were a lot of actors leveraging it for bug bounty or vulnerability research, rapidly accelerating their capabilities of discovery and disclosure. Each category demonstrates how threat actors are currently leveraging AI. Within each category is a wide disparity in sophistication based on the knowledge level of the actors involved. We tried to include use cases to cover the breadth of what we found. Takeaways and high-level findings With the recent disclosures from Hugging Face and OpenAI , it's clear the era of agentic attackers has effectively arrived. In that incident, the models were operating inside a sanctioned evaluation with safeguards deliberately relaxed — but they autonomously escaped their sandbox, found and chained real vulnerabilities, and compromised production infrastructure to reach their objective. The capabilities exist; the only missing ingredient is malicious intent, and it's a matter of time before threat actors supply it. For defenders, this is a wake-up call: Vulnerabilities will surface faster, exploitation will happen sooner, and the actors behind it won't need rest or downtime. As the case studies below show, the central challenge for guardrails right now is supporting legitimate dual-use work — red teaming and vulnerability research — without empowering malicious actors. One of the immediate takeaways is that guardrails are not functioning as expected. We did not encounter any sophisticated encoding or techniques designed to trick the models — most of the time it was a simple “I'm allowed to do this,” and the model complied. When guardrails did engage, they accomplished little. In one instance, we watched an actor abandon a censored model and pivot to an uncensored version, which completed the task without question. In another, a model pushed back on a distributed denial-of-service (DDoS) operator, but by that point the tooling had already been built. This wasn't specific to a single model or platform; it was across the board. The other big takeaway is that an actor's skill level largely determines how effectively AI can be leveraged and how much impact it u…
Tenable spent 30 days running frontier AI models against our own code. It didn’t just find bugs — it proved they’re real, with reproducible exploits. That fundamentally changes code security from ranking potential code defects to a much higher signal focused on the findings that matter. Read on to learn how it reshaped our security team's work, what it cost, and why your program is next. Key takeaways: Now code security starts with proof, not suspicions. Frontier AI instantly builds working exploits and proves which flaws are genuinely dangerous in your source code. Now remediations are confirmed issues, not just ranked lists of maybes. The durable asset is the harness, not the model. Frontier AI models get the attention, but the durable asset for security teams is the harness: the orchestration and systems around the model that turn suspected flaws into proven, reproducible exploits engineers can act on. Frontier AI doesn’t replace senior researchers; it makes one as productive as five. The scarce resource is still the expert who writes the threat model and judges what’s real. Buy the compute without funding that person, and you get a very fast way to generate findings no one can use. We’ve been running Claude Mythos Preview against our own code now for over 30 days, and one thing is crystal clear: Code security is fundamentally changing, and we believe there’s no turning back. At Tenable, our security team already had security testing agents that drove our applications, exercised API endpoints, and ran our predefined checks. But until recently, the agents couldn’t handle the harder part of code testing: finding previously unidentified flaws and proving their exploitability. As part of our work testing Anthropic’s Claude Mythos Preview for Project Glasswing , we built an agentic code security harness and powered it with this Anthropic frontier LLM, running it against our code and service repositories with pinned commits to ensure reproducible results. The work of code security is changing, but not in the way hype-driven blogs suggest. And certainly not for free. We’ve found the costs are measured in two currencies: dollars and senior-engineer hours. What follows is an account from the security practitioner’s perspective of Tenable’s internal security team and how we leveraged frontier AI: where the model proved its value, where it fell short, and what you should consider before investing further. From ranking guesses to ranking proof with frontier AI For well over a decade, the scarce resource for security teams was analyst attention. We built a whole discipline around it — reachability heuristics, exploitability guesswork, etc. — all to decide what a human security analyst should look at first in your own code. Now a frontier AI model in a harness collapses that. The ranking doesn’t go away, it gets a proof instead of a guess. When a finding arrives with a working exploit, you’re no longer ranking by how likely it is to matter; you’re ranking by what you’ve already proven does. When an LLM can surface a suspected flaw and drive a working exploit against a running build, the difficult question is no longer “which among the thousands of static findings deserves a human first?” It becomes “which of the code defects are real, and can we prove it?” A bug that shows up with a reproducible proof-of-concept sorts itself. One that can’t be reproduced goes to a validation queue. This does not mean fewer bugs. In fact, it means many more findings, especially early on, because the model surfaces threats and exposures in your source code that traditional tooling never would. Instead, what changes with frontier AI is that practically all of the bugs that reach a human analyst arrive pre-sorted by proof instead of by score. It becomes a short list of things you can already reproduce, plus a holding pen of candidates the harness is still chewing on. The model gets the headlines; the harness does the work This is not to say that the model do…
CVE-2026-44090 is a critical vulnerability in Phoenix Contact CHARX SEC-3150 devices caused by missing authentication on the MQTT broker. This allows unauthenticated remote attackers to access the broker and potentially cause denial-of-service conditions. The vulnerability can be exploited by network-adjacent attackers without any authentication. No patch or official fix information is currently available.
Bulletin ID: 2026-064-AWS Scope: AWS Content Type: Important (requires attention) Publication Date: 07/23/2026 11:30 AM PDT Description: Smithy-RS is a Rust code generation and runtime framework that generates HTTP clients and servers from Smithy interface definitions, powering the AWS SDK for Rust and custom service implementations. We identified CVE-2026-16756 where the allocation of resources without limits in the default aws-smithy-http-server serve() path allows unauthenticated "Slowloris" denial of service. Impacted versions: aws-smithy-http-server <= 0.66.4 Please refer to the article below for the most up-to-date and complete information related to this AWS Security Bulletin.
Attackers could send waves of malicious payloads to trigger buffer pre-allocations that are not freed, exhausting server memory. The post OpenSSL Silently Fixes ‘HollowByte’ DoS Vulnerability appeared first on SecurityWeek .
The HollowByte vulnerability in OpenSSL allows unauthenticated attackers to cause a denial-of-service (DoS) by sending a crafted 11-byte payload that triggers excessive memory allocation during the TLS handshake. This flaw causes the server to allocate memory based on a declared message size before receiving the full payload, leading to heap fragmentation and memory bloat that persists until the process is restarted. The issue has been fixed in OpenSSL 4.0.1 and backported to several earlier versions.
This vulnerability allows local attackers to create a denial-of-service condition on affected installations of AnyDesk. An attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit this vulnerability. The ZDI has assigned a CVSS rating of 4.7. The following CVEs are assigned: CVE-2026-15681.
