incidents in the last 24 months, +161% on the period before
┤ observatory · live ├
$ radar --set all --by incident-date --window 24m
[ ok ]real-world
[ ok ]sources
[ -- ]disclosed CVEs
[ ok ]last sweep2026-10-19 13:12Z
01 The trend

By incident date, November 2024 to October 2026. Amber: three-month average. Dashed: linear trend (-0.11 a month). The bracketed months are provisional.

amber: three-month average · dashed: linear trend
0102030peak 25Jan ’25Apr ’25Jul ’25Oct ’25Jan ’26Apr ’26Jul ’26Oct ’26
Oct ’26 so far
+178%
Trailing 12 months
−26%
Year to date
−30%
Real-world in view
severity 3–4
02 What kind

The stream is the same months as above, split into families: its height is the volume, its bands are the mix. Beside it, each family now against the window before. Hover a band for the month, click it for the incidents; the legend counts open each family.

Jan ’25Apr ’25Jul ’25Oct ’25Jan ’26Apr ’26Jul ’26Oct ’26

Movers

24 months beforelast 24 months
03 Who and how bad

The radar is the window's shape against the window before. The heatmap is where each family lands on the severity scale, from research demonstrations to confirmed real-world loss. The bubbles are who is hit by what, and the flow below follows every incident from attack on AI or by AI, through its family, onto its targets.

The shape of the threat

3876114152Prompt injection & jailbreaks · 24 months before: 13Infrastructure vulnerabilities · 24 months before: 1Data & model exposure · 24 months before: 7Agent hijacking · 24 months before: 0Supply chain & poisoning · 24 months before: 18Deepfakes & social engineering · 24 months before: 57AI-powered offense · 24 months before: 6Other · 24 months before: 4Prompt injection & jailbreaks · last 24 months: 29Infrastructure vulnerabilities · last 24 months: 11Data & model exposure · last 24 months: 17Agent hijacking · last 24 months: 15Supply chain & poisoning · last 24 months: 17Deepfakes & social engineering · last 24 months: 152AI-powered offense · last 24 months: 33Other · last 24 months: 3

Solid: last 24 months. Dashed: the 24 months before.

How bad, by family

1234all
all277

1 research · 2 disclosed · 3 exploited · 4 real loss

Severity 2 here means a flaw that was reported as news. CVEs with no confirmed exploitation are tracked separately below.

Who is hit, by what

target · incidents

An incident can name several targets.

The flow: on AI or by AI, into a family, onto its targets

Attack on AI → Prompt injection & jailbreaks: 25Attack on AI → Infrastructure vulnerabilities: 11Attack on AI → Data & model exposure: 16Attack on AI → Agent hijacking: 12Attack on AI → Supply chain & poisoning: 14AI-enabled attack → Prompt injection & jailbreaks: 4AI-enabled attack → Data & model exposure: 1AI-enabled attack → Agent hijacking: 3AI-enabled attack → Supply chain & poisoning: 3AI-enabled attack → Deepfakes & social engineering: 152AI-enabled attack → AI-powered offense: 33AI-enabled attack → Other: 3Prompt injection & jailbreaks → Individuals: 3Prompt injection & jailbreaks → Enterprise deployment: 12Prompt injection & jailbreaks → Framework / tooling: 10Prompt injection & jailbreaks → OpenAI: 4Prompt injection & jailbreaks → Anthropic: 4Prompt injection & jailbreaks → Open-weight model: 4Prompt injection & jailbreaks → Google: 1Prompt injection & jailbreaks → Microsoft: 4Infrastructure vulnerabilities → Enterprise deployment: 1Infrastructure vulnerabilities → Framework / tooling: 7Infrastructure vulnerabilities → Anthropic: 1Infrastructure vulnerabilities → Microsoft: 2Data & model exposure → Individuals: 1Data & model exposure → Enterprise deployment: 10Data & model exposure → Framework / tooling: 3Data & model exposure → OpenAI: 4Data & model exposure → Anthropic: 3Data & model exposure → Open-weight model: 4Data & model exposure → Google: 2Data & model exposure → Microsoft: 2Data & model exposure → Meta: 1Data & model exposure → xAI: 1Data & model exposure → Mistral: 1Agent hijacking → Enterprise deployment: 10Agent hijacking → Framework / tooling: 2Agent hijacking → OpenAI: 5Agent hijacking → Anthropic: 4Agent hijacking → Meta: 2Supply chain & poisoning → Individuals: 1Supply chain & poisoning → Enterprise deployment: 2Supply chain & poisoning → Framework / tooling: 11Supply chain & poisoning → Anthropic: 3Supply chain & poisoning → Open-weight model: 5Supply chain & poisoning → Google: 2Supply chain & poisoning → Amazon: 3Deepfakes & social engineering → Individuals: 143Deepfakes & social engineering → Enterprise deployment: 14Deepfakes & social engineering → OpenAI: 2Deepfakes & social engineering → Anthropic: 1Deepfakes & social engineering → Google: 2Deepfakes & social engineering → Microsoft: 1Deepfakes & social engineering → Meta: 5Deepfakes & social engineering → Not applicable: 1AI-powered offense → Individuals: 4AI-powered offense → Enterprise deployment: 19AI-powered offense → Framework / tooling: 6AI-powered offense → OpenAI: 7AI-powered offense → Anthropic: 5AI-powered offense → Open-weight model: 4AI-powered offense → Google: 3AI-powered offense → Microsoft: 1AI-powered offense → Meta: 1AI-powered offense → Amazon: 1AI-powered offense → xAI: 1AI-powered offense → Not applicable: 1Other → Individuals: 1Other → Enterprise deployment: 1Other → OpenAI: 3Other → Google: 1

Ribbon width is the count. The right column counts target mentions, so an incident naming two targets appears in both.

04 Disclosed, not exploited

CVEs and advisories for AI frameworks and tools with no confirmed exploitation. They show how much attack surface is being found and patched, not attacks, so none of them are in the counts above. A CVE joins the incidents once CISA or the vendor confirms exploitation.

050100150200peak 200Jan ’25Apr ’25Jul ’25Oct ’25Jan ’26Apr ’26Jul ’26Oct ’26
Disclosed in view
277 incidents
Busiest day
on 2025-03-20

Most disclosures, by product

05 Context

Not incidents: a weekly reading of how much web traffic is automated and which AI crawlers send it, from Cloudflare Radar. Background for the counts above, never added to them.

  • Bot share is the part of all HTTP requests Cloudflare classifies as automated, over the last seven days.
  • AI crawlers are the slice of that bot traffic sent by known AI user agents; their share of all requests is derived from the two, not measured.
  • Crawl purpose is what those crawlers declare they fetch pages for: model training, search, or acting on a user’s request.

AI traffic signal

Cloudflare Radar · last 7d

2026-10-18
bot share of requests
39.3%
−0.5% vs prior week
AI crawlers ≈ of requests
6.9%
derived: bot share × AI-crawler share
top AI crawlers · share of AI-bot traffic
  • Googlebot
    23.6%
  • ClaudeBot
    13.3%
  • Meta-ExternalAgent
    8.3%
  • Bingbot
    7.8%
crawl purpose
Training 42%Mixed Purpose 39%Search 12%User Action 5%Undeclared 2%

Source: Cloudflare Radar (CC BY-NC 4.0). Context only; not part of the incident dataset.