AI Drives Surge in Vulnerability Disclosure Rates
Google Threat Intelligence Group (GTIG) analyzed vulnerability disclosures from January 2025 through August 2026 and found that the number of vulnerabilities disclosed each month doubled during 2026. Monthly disclosures rose from 5,045 in January 2026 to 10,477 in July, peaking at 10,740 in August. This represents a significant increase in the pace of vulnerability discovery across software ecosystems.
The researchers note that raw disclosure numbers can be inflated by automated CVE assignment processes in open source projects. For example, vulnerabilities mentioning the Linux kernel alone accounted for approximately 5,000 CVEs between January and August 2026, yet none of these were observed being exploited in the wild.
High-Risk Vulnerability Disclosures Increase Sharply
Using GTIG’s internal risk ratings — which differ from CVSS scores — high-risk vulnerability disclosures grew 167% over the same period, increasing from 131 in January 2026 to 350 in August. This suggests that while volume is rising, a growing proportion of disclosed flaws pose significant potential impact.
Despite the increase in disclosures, actual exploitation remains relatively rare. GTIG recorded 141 distinct exploited vulnerabilities in the first eight months of 2026, which exceeds the 127 exploited throughout all of 2025. This translates to an average of 18 exploited vulnerabilities per month in 2026, up from 10.5 per month in 2025.
Still, only 0.23% of disclosed vulnerabilities in 2026 — roughly one in every 431 — were observed being exploited in the wild, indicating that the majority of disclosed flaws do not reach active attack use.
Zero-Day Exploitation Sees Modest Increase
Zero-day exploitation increased only marginally, rising from an average of eight per month in 2025 to 11 per month in 2026. However, August 2026 saw a spike to 22 zero-days exploited in a single month. Zero-day vulnerabilities made up 62% of all exploited vulnerabilities between January and August 2026.
GTIG attributes much of the growth in exploitation to the weaponization of n-days — known vulnerabilities for which patches exist but are not yet applied. The report suggests threat actors are increasingly using large language models (LLMs) and AI tools to automate the analysis of patches, version differences, and proof-of-concept code to rapidly develop exploits for n-days, rather than investing in the discovery of new zero-days.
AI-Discovered Vulnerabilities Show Distinct Risk Profile
GTIG identified a subset of vulnerabilities likely discovered using AI agents between January and August 2026. These AI-discovered flaws exhibit a different risk distribution compared to those found through traditional methods: 39% were rated low-risk and 58% medium-risk, compared to 69% low-risk and 28% medium-risk for non-AI-discovered vulnerabilities.
This shift likely reflects how AI research programs are configured — often tasking AI agents to audit critical infrastructure and privilege boundaries with an emphasis on finding higher-impact flaws. Consistently, half of the AI-discovered vulnerabilities resulted in remote code execution, compared to just 26% of non-AI-discovered vulnerabilities.
GTIG attributes this to AI models’ strength in identifying complex memory corruption and logic flaws that traditional static analysis tools often miss. The group has confirmed in-the-wild exploitation of at least one AI-discovered vulnerability: CVE-2026-1731, an unauthenticated OS command injection in BeyondTrust Privileged Remote Access and Remote Support.
This flaw was discovered autonomously by the Hacktron AI research agent. One threat cluster exploited it within four days of public disclosure, and five additional groups followed within seven days, demonstrating rapid weaponization potential.
AI Infrastructure Vulnerabilities Rise, But Exploitation Remains Rare
Disclosures of vulnerabilities within AI systems themselves are also increasing. GTIG tracked 2,076 AI-related CVEs between January 2025 and August 2026, including over 1,500 disclosed in 2026 alone. Roughly half of these AI-related flaws affect AI orchestration frameworks such as Langflow and LiteLLM.
Despite the growing number of AI-specific vulnerabilities, only a handful have been confirmed as exploited in the wild. GTIG has not yet observed zero-day exploitation targeting AI infrastructure or orchestration platforms.
Outlook: Continued Growth in Discovery and Exploitation Expected
GTIG concludes that the integration of AI into vulnerability research is measurably changing both the pace and profile of discovery. The report states: ‘AI is measurably changing not just the pace of vulnerability discovery and exploitation, but also the types and typical risk profiles of vulnerabilities that are being discovered.’
The group expects that rates of vulnerability discovery and exploitation will likely continue to increase in the short to medium term as AI-assisted research becomes more widespread among both defenders and attackers.
Source: SecurityWeek