Google's threat intelligence group reports CVE disclosures doubled in 2026 and profiles likely AI-discovered vulnerabilities

Key facts

As stated in the sources, with where to find them.

• Disclosures per month: 5,045 in January 2026, 10,477 in July, 10,740 in August 2026. GTIG cautions that raw volume can mislead: vulnerabilities whose description contains "Linux Kernel" generated about 5,000 CVEs from January to August 2026, with no exploited zero-days observed.Key findings; CVE Disclosure Doubled in 2026
• GTIG recorded 141 distinct vulnerabilities disclosed and exploited from January to August 2026, against 127 for all of 2025; the monthly average rose from 10.5 in 2025 to 18 in 2026. Only 0.23% of vulnerabilities disclosed in 2026 (roughly 1 in 431) were observed exploited.In-the-Wild Exploitation
• Zero-day exploitation averaged 8 per month in 2025 and 11 per month from January to August 2026 (8 to 12 per month through mid-2026, 22 in August); zero-days were 62% of exploited vulnerabilities in that period. GTIG suggests the main source of exploitation growth was rapid weaponization of n-days and says it is possible attackers use AI tools to diff patches and PoC code; GTIG offers no measurement of this.Zero-Day Exploitation Remains Stable; Are Threat Actors Finding More Success with Exploiting N-Days?
• Using GTIG's own risk ratings (not CVSS), High-Risk disclosures rose from 131 in January 2026 to 350 in August 2026 (+167%), still 3% of August disclosures. GTIG attributes this to more affected vendors and concentrated disclosure cycles (TOTOLINK router firmware, 75 High-Risk flaws in April and May; Oracle and Linux kernel, 128 in August). Exploited High-Risk vulnerabilities rose from 28 in 2025 to 75 from January to August 2026.Distinguishing Threat Risk from CVE Inflation; CVE Exploitation Trends Toward Higher Risk Vulnerabilities
• For vulnerabilities GTIG identified as likely AI-discovered versus not, January to August 2026: Low 39% vs 69%, Medium 58% vs 28%, High 4% vs 3% (Table 1). Remote code execution 50% vs 26%; information disclosure 8% vs 18%; data manipulation 5% vs 9%. The post gives no count of AI-discovered vulnerabilities and says public data undercount them; GTIG says the skew likely reflects how researchers scope and task AI agents.AI as the Hunter: Risk Profile Divergence; Table 1; Figure 7
• CVE-2026-1731 (BeyondTrust Privileged Remote Access and Remote Support, unauthenticated OS command injection) was discovered autonomously by a third-party research agent (Hacktron AI). GTIG observed one threat cluster exploiting it within four days of public disclosure and five more within seven days, with post-exploitation including data exfiltration.AI as the Hunter: Risk Profile Divergence
• GTIG tracked 2,076 cumulative AI-related CVE disclosures from January 2025 to August 2026, over 1,500 of them from January to August 2026 across eight layers. Agent orchestration and framework flaws numbered 782 (50% of AI-related flaws, +347% in 2026); inference and serving infrastructure 212, of which 24% stemmed from unauthenticated API endpoints or SSRF.AI as the Hunted; Table 2; Emerging Battlegrounds
• GTIG says zero-day exploitation of AI infrastructure had not been observed and only a handful of the 2,076 AI-related CVEs were confirmed exploited in the wild, listing CVE-2026-42271 (LiteLLM MCP server preview endpoint command injection), CVE-2026-5027 and CVE-2025-3248 (Langflow).Active In-the-Wild Exploitation of AI Middleware
• GTIG expects vulnerability discovery and exploitation to keep growing in the short to medium term, calls the public data early, and recommends threat-intelligence-driven triage with automated, agentic remediation instead of unprioritized mass patching.Introduction; Outlook

Findings that cite this record

Key questions this bears on

• Is AI shifting the balance between finding and fixing vulnerabilities?Discovery is ahead. AI finds real vulnerabilities faster than they are fixed, and simple checks overstate how often AI patches work.
• How are attackers using AI agents in real operations?Increasingly to run parts of intrusions: providers and vendors report agent-driven espionage, extortion and credential theft, and malware that queries LLMs.

Sources

• Vulnerability Discovery and Exploitation Trends in the AI EraGoogle Threat Intelligence GroupSep 30, 2026Primary sourceAccessed Sep 30, 2026

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