Vulnerability Management | Breaking Cybersecurity News | The Hacker News

Every patch is a confession. The moment a vendor ships a security fix, the diff between the old code and the new code tells anyone watching exactly what was broken and where. Turn that diff back into a working exploit, and you can hit every system that hasn't updated yet. This is N-day exploitation, and it's always been a race: the vendor patches, the clock starts, and defenders try to deploy before an attacker finishes reverse-engineering the fix. For the last thirty-odd years, defenders usually won that race. Reverse-engineering a patch into a reliable exploit was slow, specialized work - usually requiring weeks of expert-level effort. Historically, the gap between a patch and a working public exploit spanned weeks, often months. The traditional playbook assumed you had at least a few weeks. You don't anymore. Not even close. Reverse-engineering a patch used to take weeks. Mythos does it in an hour. Anthropic's red team measured exactly that. Given not...

Attackers have begun to exploit two critical vulnerabilities in WordPress that, when combined together, enable unauthenticated remote code execution (RCE) and complete compromise of vulnerable websites. The two security flaws, tracked as CVE-2026-63030 and CVE-2026-60137 , have been codenamed wp2shell . "By the early hours of Saturday morning (UTC), successful exploitation was already well underway, initially using public exploit code to exfiltrate hashed credentials, with remote code execution following once additional details were made public," Jake Knott, principal security researcher at watchTowr, told The Hacker News in a statement. "From our vantage point across a global client base, we are seeing widespread impact of this vulnerability across organizations of every size and every vertical." Telemetry data captured by KEVIntel shows that 13 unique IP addresses from Switzerland, Germany, the U.K., Indonesia, Lithuania, the Netherlands, and Singapore h...

The industry spent the initial months after Anthropic's April 7 Mythos reveal focused on volume . How many new CVEs would Mythos add to an already overloaded pipeline? How quickly would the flood of AI-driven discovery overwhelm triage capabilities? How long would it take adversaries to weaponize Mythos findings at scale? Those questions were and remain valid. Yet they all stop short of addressing the single metric that determines whether any of those vulnerabilities actually lead to a breach: the exposure window. The exposure window - the gap between the moment a vulnerability becomes exploitable and the moment your team fixes it - is the time an attacker has to do actual damage. That window is currently open far too wide. In 2025, the average eCrime breakout time dropped to 29 minutes . Even PCI DSS - the strictest compliance framework in the industry - allows 30 days to remediate a critical vulnerability. That's a 1,000-to-1 gap between how fast attackers move and how f...

Artificial intelligence (AI) is changing offensive security, but it has not changed the standard that matters most: a finding has to be proven before it becomes useful. AI-assisted tools can read code quickly, generate payloads, summarize attack surfaces, explain unfamiliar APIs, and run repetitive testing workflows at impressive speed. That is a real advantage for security teams. It also creates a new kind of pressure, because the industry can now produce more vulnerability-looking output than ever before. The problem is that output is not the same as evidence. A generated report can sound polished, include a severity rating, and even contain a proof-of-concept that looks reasonable at first glance. None of that proves the bug exists in the deployed environment. None of it proves exploitability, impact, or risk. In offensive testing, the hard part has never been writing something that sounds like a vulnerability report. The hard part is demonstrating what is actually true. That d...

AI security agents are starting to influence real security decisions. They summarize findings, prioritize remediation, recommend next steps, and help teams move faster. But most still rely on fragmented risk signals: scanner output, severity scores, threat intelligence, configuration findings, and exposure data. That fragmentation matters because attackers do not move through environments one tool category at a time. They chain exposures across identities, networks, cloud assets, applications, and security controls. If the AI workflow only sees isolated findings, it cannot understand whether those findings create a real attack path. As AI-powered attackers accelerate exploitation, security teams need more than faster AI-assisted workflows. They need workflows grounded in evidence that can prove which risks are exploitable. These systems can correlate information and identify patterns, but without validation, they cannot answer the question security teams ultimately care about: C...

Software supply chain security was hard enough. Then AI joined the build pipeline. For five years, "software supply chain security" meant one question: what's in your code? Which open-source packages, which versions, which transitive dependencies three layers deep that nobody chose on purpose? SolarWinds, Log4Shell, and XZ Utils all taught the same lesson: the risk lives less in the code a team writes and more in everything that produces it. Shai-Hulud, the self-propagating malicious package campaign that spread through developer toolchains this year, taught the next one: knowing what's in your code is still necessary, but it's no longer sufficient. In the roughly 20 months since the Model Context Protocol launched, AI tools, models, and the infrastructure around them have become load-bearing parts of how software gets built, deployed, and run. Code is written by agents. Packages are pulled in by autonomous tools that decide they are needed. Prompts have...

Researchers tested 444 AI chatbot apps for iPhone and found that 282 of them, nearly two-thirds, exposed paid AI access through their network traffic. In many cases, the path in was visible just by watching what the app sent: a plaintext API key, a reusable token, or a backend server that accepted requests with no key at all. Whoever grabs it can send model requests on the developer's account, and the developer pays the bill. Three months after the researchers warned the developers, only 28% had fixed it. The work, from researchers at Wake Forest University, is the  first in-depth study of the problem on iOS . It is striking partly because of how little effort the snooping took. The team used a tool they built, LLMKeyLens , that watches an app's traffic and pulls out the credentials as they go by. No jailbreaking, no cracking the app open. The key is the secret that lets the app call a service like OpenAI or Google Gemini. Embed it in the app, and it is exposed with ev...

We are standing at the end of an era we never thought to mourn: the era of human-speed threats . For years, cybersecurity moved to a rhythm organizations could follow. A researcher found a bug, a CVE was cataloged, a vendor navigated a patch cycle, and weeks or even months later, a fix was deployed. In this era, dwell time was measured in days, sometimes weeks. We are now approaching an inflection point in the threat timeline unlike any that came before it. The trigger was the emergence of frontier agentic models in early 2026: AI entities that no longer just suggested code, but actively tested it. These models don't merely accelerate the offensive lifecycle; they radically compress the time between discovery and weaponization. The predator wearing a productivity badge There is a reason the old saying warns about the wolf in sheep’s clothing. In the scramble to stay competitive, organizations have handed AI the keys to the deepest layers of their infrastructure: granting L...

OpenAI on Monday said it's releasing an improved version of its GPT‑5.5‑Cyber model to trusted defenders as part of the Daybreak initiative  the artificial intelligence (AI) company announced last month. Calling GPT‑5.5‑Cyber its "strongest model yet for finding and helping patch software vulnerabilities," OpenAI said the model can "sustain deeper analysis across large codebases" to identify security issues, validate them in a controlled environment, and develop and test patches. In tandem, the tech upstart is releasing an update to the Codex Security plugin⁠ to speed up the process of discovering and patching vulnerabilities in existing systems, alongside preventing new vulnerabilities from entering production codebases. "Developers can run deep scans or review recent changes, generate reports with severity, affected code locations, validation evidence, and remediation guidance, trace attack paths, build threat models, validate findings, and genera...

Introduction The average enterprise security team has 40 or more security tools, giving a lot of visibility into internal telemetry and asset data. But often, these tools are working in siloes, generating (overlapping) alerts and data. And yet, breach dwell times remain stubbornly long (~43 days), response windows keep closing before teams can act, and analysts burn out triaging noise instead of stopping threats. The problem isn't effort. It's architecture. Security programs were built for a world where threats moved slowly enough for humans to coordinate responses manually. That world no longer exists. With the way AI capabilities are getting developed and used, especially with frontier AI tools, a much more proactive stance to security is needed as well as machine speed response to combat fast moving adversaries. Gartner's Continuous Threat Exposure Management (CTEM) framework helps this shift from reactive, point-in-time assessments to a continuous, iterative cycl...

For security teams, the findings never stop, but confidence in knowing which ones matter is becoming harder to maintain. The problem is no longer visibility. It's validation. Security teams must decide which findings warrant action while operating under constant pressure and incomplete information. Increasingly, the challenge is not discovering potential risks. It is determining which risks deserve attention first. Visibility Got Us Here. Validation Moves Us Forward. The security industry has spent the better part of a decade improving visibility. Vulnerability scanners, cloud security posture tools, endpoint detection, attack surface platforms, code analysis, and threat intelligence feeds all contribute to a more complete understanding of the attack surface. The investment has been enormous, and it has largely worked. Modern enterprises can see their environments in ways that would have seemed remarkable ten years ago. Yet improved visibility has not automatically transla...

Breaches don't always start with a zero-day. An exposed admin panel can get brute-forced, or credentials reused from a previous attack. But when a vulnerability does drop — like MongoBleed earlier this year, which let attackers pull credentials and session tokens from server memory without authentication — anything internet-facing is immediately at risk. With time-to-exploit now down to a single day, the question isn't just how fast you can patch. It's why the service was exposed in the first place. The team at Intruder analyzed 3,000 attack surfaces to find out how much of a typical organization's attack surface consists of services that have no reason to be there. We grouped what we found into four categories — HTTP panels, risky ports and services, databases, and publicly accessible files and information. The full findings, including breakdowns by company size and industry, are in our 2026 Attack Surface Management Index . How widespread is the problem? ...

Instead of hiding on the laptops and servers defenders watch most closely, a China-nexus group spent close to a decade hidden inside the Linux login system itself. Sygnia, which tracks the group as Velvet Ant , says it backdoored the PAM and OpenSSH components that decide who is allowed to sign in, planting its access where ordinary cleanup could not reach it. The network it targeted had no direct internet access, so the group first staged through internet-facing systems to get there. The earliest traces go back to 2016. Instead of dropping new malware that a scanner might catch, the attacker changed the trusted login programs themselves. Nothing obvious appeared, and no exploit was needed, so the activity looked like normal administration. On many machines, the attacker replaced the main PAM login module with backdoored copies. Some let them in with a secret password; others quietly recorded real usernames and passwords as people logged in. Researchers found nine separate ver...

Assume the breach. Zero-days keep shipping, AI is writing exploits faster than anyone patches, and "patch everything in time" stopped working years ago. Stop betting the org on winning that race. You don't control which bug lands. You control what it can reach once it does. That is a question about the shape of your network, and most teams have the shape wrong. HD Moore, creator of Metasploit and now CEO of runZero, spends the session showing you that shape from the attacker's side. Save your seat for a LIVE session , or register, and we will send you the recording. The segmentation you think you have The comfortable assumption: critical systems sit behind a firewall or off on their own segment, so a foothold over here cannot become a disaster over there. Call it the segmentation illusion. It holds until someone maps the network for real. Then the seams show up. A device wired into two networks at once, quietly bridging the zones you meant to keep apart. Co...

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Monday added a high-severity security flaw impacting Oracle WebLogic Server to its Known Exploited Vulnerabilities ( KEV ) Catalog, based on evidence of active exploitation. The vulnerability, CVE-2024-21182 (CVSS score: 7.5), allows an unauthenticated attacker with network access to take control of susceptible servers. It was patched by Oracle in July 2024. "Oracle WebLogic contains an unspecified vulnerability that could allow an unauthenticated attacker with network access via T3, IIOP to compromise Oracle WebLogic Server," CISA said. "Successful attacks of this vulnerability can result in unauthorized access to critical data or complete access to all Oracle WebLogic Server accessible data." There are currently no public reports about how the vulnerability is being exploited in the wild. That said, prior flaws in the software have been repeatedly weaponized by various threat actors to...

AI-driven exploitation timelines are rapidly shrinking, and they are not going to stop shrinking. Vulnerabilities are being discovered, reproduced, and weaponized faster than ever in the history of enterprise security. As a result, the window between a vulnerability being disclosed and indiscriminate exploitation observed across the internet is now measured in hours, not days. The industry's main answer has largely been: patch faster. Regulators say it, boards expect it, and executives demand it. But for most enterprises, it is not a button defenders can press. Patching is a controlled process shaped by uptime requirements, stability testing, change windows, business approvals, compliance obligations, and the reality that production systems cannot be broken in the name of urgency. While patching is still essential, patching alone or even faster patching is no longer a complete answer to this "new normal" and influx of disclosed vulnerabilities. Anthropic's Proj...

The Indian Computer Emergency Response Team (CERT-In) has issued new guidelines requiring organizations to patch critical security vulnerabilities in internet-exposed systems within 12 hours of being flagged where "feasible" to safeguard against potential threats stemming from threat actors' abuse of artificial intelligence (AI) tools and large language models (LLMs) to automate vulnerability discovery and exploitation, and enhance the scale and velocity of cyber attacks. "AI-assisted cyber exploitation reduces the time required for adversaries to identify, weaponize, and exploit vulnerabilities, exposed services, weak identities, insecure APIs, and misconfigured systems," CERT-In said in a 38-page blueprint published Monday. "As organizations become increasingly dependent on interconnected digital infrastructure, cloud ecosystems, software supply chains, operational technologies, and AI-enabled platforms, the potential impact of AI-enabled cyber thr...