Vulnerability Management | Breaking Cybersecurity News | The Hacker News

AI-driven vulnerability discovery is no longer a research project. Claude Mythos proved that. In a single sweep, it uncovered thousands of vulnerabilities in software we use every day, generated working exploits, and exposed bugs that had survived decades of human review. Other AI models are rapidly catching up, and we've entered into an entirely new operating environment for cybersecurity. The industry is treating this as a turning point, and it is. But not for the reason most people might think. The Real Problem Was Never Finding Vulnerabilities Most of the conversation around AI security focuses on discovery: AI can now identify vulnerabilities faster than human teams ever could. That is certainly true, but it also misses the larger operational reality organizations have been struggling with for years. Security teams were already overwhelmed long before AI entered the picture. Vulnerability scanners, fuzzers, and static analysis tools have consistently generated more...

Attackers embraced AI in 2024. They are running attacks at agentic speed now. Security operations mostly aren't moving at the same pace. The mismatch between attack speed and response speed is now the most exploitable condition in most environments.  We recently ran an analysis on healthcare organizations using Check Point Exposure Management . One tertiary hospital had reduced its mean time to remediate (MTTR) to 0.87 hours. Zero IPS bypass events. 100% hardening effectiveness. Sub-one-hour MTTR, at scale, in a regulated healthcare environment where change control alone used to take days. We did not get there from patching faster. It came from changing the model entirely. The Asymmetry Nobody Talks About The security industry spent years optimizing detection. Feed more signals into SIEM, add more correlation rules, build bigger dashboards. Detection got faster. But remediation stayed manual, sequential, and slow. Meanwhile, attackers didn't wait. They adopted agentic to...

Most of the commentary on Anthropic's Claude Mythos Preview has gone in one of two directions: one camp treats it as the civilizational inflection point, the other as marketing dressed up as a research result. Neither read is particularly useful for a security leader who still has a program to run on Monday. The AISLE team's technical response to the Mythos announcement made a fair point worth sitting with: much of what was demonstrated is recoverable on smaller, open-weight models, particularly on the discovery side. Early testing results of OpenAI's GPT 5.5 show CTF performance close to or slightly superior to Mythos; the exclusivity framing is arguable, but the accelerated model improvement in offensive security is undisputable. The UK AI Security Institute found that Mythos can autonomously execute a complete corporate network takeover, succeeding in 30% of its attempts on a complex attack range — a task AISI estimates would require roughly 20 hours for a human e...

Continuous Threat Exposure Management (CTEM) has moved well past buzzword status. We've talked about this before . It's true that in the past years, Gartner has been making these grand predictions about its benefits: organizations prioritizing CTEM investments will suffer two-thirds fewer breaches by 2026 … Well, we're now in 2026 and, in reality, SOC teams are still facing the same dilemma: more exposure data than they can act on, and no reliable way to decide what actually matters. 96% of security teams face challenges trying to validate whether their security risks are exploitable, while 2 in 3 state that they don't have a consolidated view of their cyber risk exposure. - Filigran-comissioned third-party market survey on exposure validation  It's pretty clear now that to actually benefit from CTEM, organizations needs to first utilize their cyber threat intelligence better. It is not just about better asset, vulnerability management or dealing with a single CTI provider, b...

Vulnerability management is the continuous process of identifying, assessing, prioritizing, and addressing security weaknesses across systems, applications, and infrastructure. It extends beyond periodic scanning; it includes validating findings, understanding exposure in real-world environments, and tracking remediation over time. Effective vulnerability management combines asset visibility, vulnerability intelligence, and operational context to determine which flaws present actual risk rather than theoretical exposure. Modern IT environments further complicate the process of vulnerability management. Hybrid IT infrastructure, third-party dependencies, and internet-facing services increase the attack surface while generating large volumes of vulnerability data. Security teams must balance operational constraints, such as out-of-support legacy systems and uptime requirements, with the need to quickly reduce exposure. As a result, vulnerability management is no longer limited to coun...

Most application security (AppSec) teams know their OWASP Top 10, the industry-standard list of the most critical software security risks. Fewer know which of those categories their organization actually fixes. In conversations with security teams, I hear the same story: "We prioritize criticals, so the important stuff gets handled." The data tells a different story. Fix rates vary dramatically by OWASP vulnerability class, and not in the ways most teams expect. The data comes from Semgrep's Remediation at Scale report , which analyzed anonymized remediation patterns across 50,000+ repositories and hundreds of organizations during 2025. The methodology is straightforward: group organizations into two cohorts by fix rate (top 15% as "leaders," remaining 85% as "field"), then compare what each group actually does differently. The gap between leaders and the field isn't about detection quality or prioritization frameworks. Both cohorts apply the s...

When Shai-Hulud 2.0 hit in late 2025, it was a brutal, expensive wake-up call for DevSecOps teams. It showed that the industry's direction of shifting left, where teams pass security onto developers, wasn't the silver bullet everyone hoped for. Pushing that responsibility was fine in theory, but it crumbled quickly because the foundation it was built on was inherently flimsy. As we move further into 2026, we need a more definitive fix to the structural weakness in the pipelines in light of a potential Shai-Hulud 3.0. A major lesson from 2.0 was that internal CI/CD runners were easily hijacked and turned into attack botnets. Teams need to take that finding and come back with a truly proactive defense. A curated catalog is a way for security teams to control exactly what code and components enter their environment, while still giving engineering teams a fast, secure way to build - it is the key to creating a sustainable solution. More on a curated catalog later. The Anatomy o...