CVE-2017-0144

Tenable Blog · Jun 24, 2026

Over a 30 day period, Tenable detected 457 million AI-related security issues among 7,000-plus organizations, an average of 62,000 exposures per organization. If we didn’t already know that shadow AI was a problem, data like this makes it clear every organization needs to visualize, map, assess, and protect with a comprehensive exposure management program.Key takeawaysAI tools — approved and unapproved — are driving a massive wave of daily exposures, including an average of 62,000 per organization during a recent 30-day period. This is creating AI security issues that are primarily tied to misconfigurations and unmanaged dependencies rather than standard CVEs.To successfully outpace AI-assisted threat actors, security teams must deploy automated, agentic workflows that can contain and remediate critical exposures at machine speed.It’s time for security teams to shift from legacy vulnerability scanning to AI-driven, contextual exposure management that maps specific attack paths leading to their most critical assets.How much cybersecurity risk does AI create for organizations?For years, some security leaders have lived by a simple, comforting truism: If the service-level agreement (SLA) dashboard for vulnerability remediation is green, the organization is safe. By focusing on tracking CVEs and patching schedules, they believe they’re effectively managing cyber risk. Today, as AI boosts cyber threats and transforms cyber defenses, this maxim has morphed from a risky platitude into an outright dangerous fallacy. Cyber teams that operate under this model risk drowning in what Tenable calls the “vulnami” — a tsunami of CVEs fueled by AI vulnerability discovery. They also risk failing to see and address the vast expanse of non-CVE threats in their hybrid environments.In this blog, we’ll answer the question, “How much cybersecurity risk does AI create for organizations?” and we’ll unpack how exposure management can empower security teams to not only stay ahead of the CVE...

Tenable Blog · May 27, 2026

Tenable Research has developed a graph-based model linking 600+ threat groups to real-world customer exposures. It reveals which vulnerabilities sit at the intersection of severity, active exploitation, and organizational risk.Key takeawaysThe "patch everything" strategy is dead: Vulnerability prioritization based on exploitation risk offers a path forward. A directed graph model linking 600+ threat actors to vulnerabilities in 7,800 customer environments reveals that 68% of organizations carry at least one CVE previously exploited by a named adversary, and 321 tracked threat groups can reach at least one customer environment through an active vulnerability. Prevalence of "Elite Arsenal" CVEs requires immediate attention: The 242 "Elite Arsenal" CVEs — those meeting all three criteria of critical VPR (≥ 9), CISA KEV listing, and documented threat group exploitation — are nearly universally present across the studied customer base, with 241 of 242 actively detected. More than half are five or more years old, and 78% of the persistently exploited core are simultaneously weaponized by nation-state APTs, commodity malware operators, and ransomware gangs. Non-CVE exposures are universally dangerous: Non-CVE exposures, including misconfigurations, weak credentials, and end-of-life software, are present in virtually 100% of studied organizations, with 60% carrying at least one that maps to a tracked threat actor's preferred techniques. Preliminary modeling suggests these exposures may confer more breach risk than CVE-linked findings, yet no industry-standard scoring infrastructure exists to prioritize them.While the first two posts in this blog series documented the accelerating vulnerability flood and the widening remediation gap, today we answer the outstanding question: Where do these forces actually collide inside customer environments? Using a directed graph model that maps more than 600 tracked threat groups to vulnerabilities observed across 7,800 organizations,...

github · Created 2024-04-03 07:39:43 UTC · 0 stars

LAB: TẤN CÔNG HỆ ĐIỀU HÀNH WINDOWS DỰA VÀO LỖ HỔNG GIAO THỨC SMB.

github · Created 2022-12-16 11:10:13 UTC · 0 stars

Chương trình theo dõi, giám sát lưu lượng mạng được viết bằng Python, nó sẽ đưa ra cảnh báo khi phát hiện tấn công CVE-2017-0144

github · Created 2021-03-22 09:33:51 UTC · 14 stars