Defending Against AI-Driven Cyber Threats with Cloudflare

Web security in the age of AI agents, by Cloudflare: https://lnkd.in/g-mXVG_q It reads a bit like a marketing piece, but I believe the main insights are valuable - how to consider cyber security in an age where speed, scale and adaptability of attacks are a given? While the article highlights Cloudflare's solutions, the real takeaway is that architecture matters more than patch speed. Traditional signature-based security can't keep up when AI can mutate payloads dynamically. Personally, I’ve been very happy using some of their free-tier tools for domains: - DNS-level bot protection to kill automated reconnaissance early. - Locking down firewall ingress to exclusively trust Cloudflare IPs (preventing origin bypass). You can certainly find similar functionality in competing tools or open-source frameworks, but the underlying strategy is what matters: stop chasing CVEs and start hardening the perimeter against automated behavior. How about you, any good products/frameworks you have seen related to protecting sites and backend? #Cybersecurity #WebSecurity #Cloudflare #ApplicationSecurity #DevOps #CloudSecurity #AIAgents

The next generation of cyber attacks will not be written by humans. Frontier AI models are already probing defenses, adapting tactics, and finding gaps faster than any red team. Patching faster is not a strategy, but Architecture is. In our latest post, we unpack how Cloudflare thinks about defending against frontier cyber models, and why we built Project Glasswing to be the architecture that outlasts the patch cycle. We are running it as customer zero. Because if we would not trust our own network with it, we would not ask you to. Read the full breakdown: https://lnkd.in/gstHRqnB

Project Glasswing: Securing critical software for the AI era. Mythos Preview has already found thousands of high-severity vulnerabilities. As part of Project Glasswing, the launch partners listed above will use Mythos Preview as part of their defensive security work. https://lnkd.in/dqPkP8z3

Thales tested an AI agent against both an unprotected binary and one protected with Thales Sentinel LDK Envelope Plus, with stark results: the unprotected version yielded 8 out of 10 planted vulnerabilities in 3 minutes, while the protected version produced zero findings after nearly 7 hours and a 970x increase in token consumption before the AI recommended stopping. As AI-accelerated reverse engineering compresses attack timelines, software protection that makes binaries economically unviable as attack targets is no longer a nice-to-have. Read the full report: https://hubs.la/Q04l7MbK0 #AISecurity #Cybersecurity #SoftwareSecurity

Radware AI Xploit Shield delivers virtual patching for newly identified application and API flaws: Radware has announced AI Xploit Shield, a new service that provides organizations with protection for their applications and APIs from exploitation of newly discovered vulnerabilities. As emerging frontier AI models like Mythos from Anthropic accelerate vulnerability discovery, organizations face a growing challenge: the volume of newly discovered vulnerabilities is accelerating while the window between vulnerability identification and exploitation is shrinking. These trends are widening the gap between discovery and remediation and making it increasingly difficult … More → The post Radware AI Xploit Shield delivers virtual patching for newly identified application and API flaws appeared first on Help Net Security.

Anthropic just proved that 𝐍-𝐝𝐚𝐲 𝐯𝐮𝐥𝐧𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 are officially dead. Welcome to the era of the 𝐍-𝐡𝐨𝐮𝐫 𝐞𝐱𝐩𝐥𝐨𝐢𝐭. While everyone focuses on exotic 𝐳𝐞𝐫𝐨-𝐝𝐚𝐲𝐬, real-world harm lives in the gap between a 𝐩𝐚𝐭𝐜𝐡 𝐫𝐞𝐥𝐞𝐚𝐬𝐞 and its 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭. Using 𝐂𝐥𝐚𝐮𝐝𝐞 𝐌𝐲𝐭𝐡𝐨𝐬 𝐏𝐫𝐞𝐯𝐢𝐞𝐰, Anthropic evaluated 21 recent Windows kernel vulnerabilities. The model reverse-engineered the patches, found the root cause via patch diffing, and weaponized full-chain exploits in hours. The first working proof-of-concept dropped in just 12 minutes. The economics are what should scare you. It generated eight distinct privilege-escalation exploits at an average cost of just $2000 𝐢𝐧 𝐀𝐏𝐈 𝐜𝐫𝐞𝐝𝐢𝐭𝐬. AI completely eliminates the biggest bottleneck for N-day campaigns: the scarce reverse-engineering expertise needed to weaponize a disclosure. A single operator with a few thousand dollars can now turn a batch of monthly patches into active exploits in an afternoon. The traditional playbook of multi-week staged rollouts and monthly patch cadences is built for an era that no longer exists. If you are still operating inside a multi-week patch gap without automated, real-time ways to detect and block threats at the infrastructure layer, you are completely exposed. https://lnkd.in/gsFSeAU9 #CyberSecurity #Infosec #DevSecOps #PatchManagement #AI #AppSec #AISecurity #AISafety #LLM #Claude #Mythos #Fable

The hard part of security used to be finding the holes. That just stopped being true. Anthropic's Claude Mythos autonomously surfaced more than 10,000 software flaws, including a pile of zero-days across operating systems and browsers. The reported numbers are blunt: an 18x efficiency gain in discovery, 85.6% coverage of exploited vulnerabilities. Discovery is now cheap and fast. Remediation is not. This is the uncomfortable part of the article. The same AI capability that finds vulnerabilities also accelerates the exploits, so the window between "published" and "actively exploited" is shrinking. Meanwhile most enterprises still measure patching in weeks. The recommendation in the piece is to build patch governance that runs in hours, not weeks, and to trigger updates on events rather than on someone eventually noticing a ticket. The bottleneck moved. It used to be detection. Now it is the human-paced work between knowing and fixing: figuring out which of your systems is actually affected, what the change means for your code, and what to do about it. That gap is exactly what we are building Odyssey for, on the dependency side of the same problem. When a library, SDK, or API ships a breaking or security-relevant change, the question is never just "did something change." It is "which of our integrations is affected, how bad is it for us specifically, and what is the fix." Detection without that context is just a faster way to generate a backlog. Finding the problem in hours only helps if fixing it does not still take six weeks. Full article: https://lnkd.in/dAsmhd8V #CyberSecurity #DevOps #EngineeringLeadership #SoftwareDevelopment

If exploits arrive before your patches do, is your security model still valid? The no-hype AI newsletter for senior leaders. Subscribe free: https://lnkd.in/eB6xT9Cd VentureBeat reports that Anthropic's Claude Mythos has closed the gap between AI's ability to exploit known vulnerabilities and its ability to discover new ones, with several recent CVEs exploited in fewer than 24 hours and one in under 10 hours from disclosure. Here's what happened: In 2024, GPT-4 could exploit 87% of known vulnerabilities given a CVE description but only 7% without. Anthropic's Claude Mythos Preview, announced 7 April 2026, has closed that margin by autonomously discovering thousands of zero-day vulnerabilities. Mythos scored 83.1% on the @CyberGym benchmark, and in one OpenBSD campaign cost under $20,000 in compute. Real-world exploitation timelines are now collapsing: Langflow's CVE-2026-33017 was exploited 20 hours after disclosure with no public proof-of-concept; Marimo's CVE-2026-39987 was hit in 9 hours and 41 minutes. The median time from CVE publication to CISA's known exploited vulnerabilities listing is 5 days, which is now slower than active exploitation. Why this matters for your business: Traditional patch cycles are no longer fast enough. Replace CVSS-only prioritisation with a combined CISA KEV, EPSS, and CVSS filter (which delivers 18x efficiency gain and 85.6% coverage). Trigger event-driven patching for internet-exposed services within 4 hours of CVE publication. Audit AI agent authorisation boundaries for oversized requests, burst rates, and unusual parameter combinations. The number that stands out: 9 hours and 41 minutes from CVE disclosure to active exploitation on Marimo's CVE-2026-39987, the kind of timeline that makes most enterprise patching processes structurally obsolete. #AIinBusiness #CyberSecurity #Anthropic Source: VentureBeat, 31 May 2026 https://lnkd.in/dAsmhd8V

OpenAI rolled out a cybersecurity model that rivals the capabilities of Mythos, but without nearly as much political pushback as Anthropic received. The new GPT-5.5-Cyber achieved an 85.6% score in CyberGym, an internal benchmark that measures whether an AI agent can reproduce known software vulnerabilities. In comparison, Mythos 5 scored 83.8% on the same evaluation. It's very much unclear why OpenAI was able to move forward with this model release while Anthropic is still stuck fighting export controls that bar it from allowing foreign nationals to use its models. https://lnkd.in/eJhqNPkm

AI’s constant patching treadmill can be a security problem: The breakneck speed of model releases may be creating short, silent security gaps as developers must choose between performance and security, according to a new report. The post AI’s constant patching treadmill can be a security problem appeared first on CyberScoop. #cyber #cybersecurity #informationsecurity