The number of vulnerability disclosures made each month has more than doubled since the start of the year, hitting 10,740 in August, according to Google Threat Intelligence Group (GTIG).

The researchers also found the number of vulnerabilities being exploited is showing a similar trajectory. In 2025, the average was 10.5 exploits per month, this rose to 18 per month from January 2026 to August 2026.

The researchers said they expect both these trends to continue in the short to medium term.

Zero-day exploitation, however, increased only marginally, rising from an average of eight cases per month in 2025 to an average of 11 from January 2026 to August 2026.

According to GTIG, AI is changing not just the pace of vulnerability discovery and exploitation, but also the types of vulnerabilities that are being discovered.

While fewer low-risk vulnerabilities are being found, there are more moderate-risk vulnerabilities and vulnerabilities leading to remote code execution (RCE) are being unearthed.

High-risk vulnerabilities surged from 131 disclosures in January 2026 to 350 in August, up by 167%, although they still account for just 3% of all vulnerabilities disclosed. The increase, said GTIG, comes down to a widening pool of affected vendors and concentrated vendor disclosure cycles.

“It’s important to note that the proportion of vulnerabilities exploited versus disclosed remains vanishingly small: only 0.23% of all disclosed vulnerabilities in 2026 (roughly one in 431) were ever observed in active exploitation, or on the order of tens versus thousands per month,” the researchers said.

“This means that monthly exploitation counts can more easily be influenced by other factors such as vendor disclosure cycles and threat actor campaign spikes.”

Meanwhile, current public data significantly undercounts vulnerabilities discovered by AI, the researchers said. This is partly due to the absence of standardized metadata, and partly because of silent first-party and cloud patching. Major cloud and SaaS providers routinely remediate AI-surfaced vulnerabilities directly in production without requesting formal CVE IDs, with CVE assignments typically reserved for on-premises or third-party software.

The increase in vulnerability discovery, said Steve Povolny, vice president of AI strategy and security research at Exabeam, shouldn’t come as a surprise.

“We know by now that AI models are able to achieve speed, scale and efficiency that few, if any, human beings are capable of. Inevitably, discovering known flaws and finding ways to exploit them is par for the course when it comes to AI,” he said.

However, he highlighted the lack of change in zero-day exploitation.

“This leads to one of two conclusions: first, AI models are very good at uncovering known (N-day) vulnerabilities but not mature enough to discover widespread zero-day vulnerabilities of significance,” he suggested.

“The second option is that AI augmented zero-day discovery is happening en masse, but flying under the radar, based on the inherent value it provides to attackers such as nation states and zero-day bug broker groups. Given the amount of security researchers focused on leveraging AI for zero-day discovery, I think the latter is less likely.”