Threats Tagged 'dos'

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.

A denial-of-service (DoS) vulnerability exists in the in-app browser of the LINE client for iOS. The issue arises from insufficient safeguards when handling arbitrary URL schemes, which may cause the application to become unresponsive or crash. No specific affected versions or patches have been identified in the provided data. There is no evidence of active exploitation in the wild at this time.

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…

Deployment of the VPS.org one-click Supabase template deploys a PostgreSQL instance that is published on all interfaces (0.0.0.0:5432) with a default database password set to "postgres". Because Docker installs its own iptables rules, this exposure bypasses a standard host UFW configuration.

CVE-2026-18140 is a high-severity vulnerability in the aws-smithy-json runtime crate before version 0.62.7. It involves uncontrolled recursion in the unknown-key skip path used by smithy-rs generated struct deserializers. This flaw allows remote unauthenticated attackers to cause a denial of service by sending a single HTTP request with deeply nested JSON, leading to process abort via stack exhaustion. The issue affects aws-smithy-json versions prior to 0.62.7. AWS manages remediation for this cloud-hosted service, and users should upgrade to version 0.62.7 or later and rebuild to mitigate the risk.

CVE-2026-44090 is a vulnerability in Phoenix Contact CHARX SEC-3150 devices where missing authentication allows unauthenticated remote attackers to access the MQTT broker. This can lead to denial-of-service conditions. The vulnerability does not require authentication and is exploitable by network-adjacent attackers. No patch or official fix information is currently available. The vulnerability is rated critical based on the provided severity and CVSS 4.0 vector.

CVE-2026-44107 is a high-severity vulnerability in the Phoenix Contact CHARX SEC-3150 charging controller. It allows an unauthenticated attacker to trigger a reboot of the device via Modbus TCP when the Modbus service is enabled. This results in a denial-of-service (DoS) condition by disrupting the charging controller's operation.

A vulnerability in the `zipx.Unzip` extraction routine of Develar's app-builder allows an attacker to overwrite arbitrary files on macOS APFS by exploiting a Unicode Normalization Collision combined with symlink following behavior. APFS treats certain Unicode equivalent filenames as identical (e.g., ß ↔ ss), while app builder performs no canonical normalization before validating or writing paths. As a result, a crafted ZIP archive containing: • a symlink entry named ss pointing to a target file, and • a regular file named ß containing attacker controlled data, will cause the second write to follow the symlink and overwrite the target file.

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.