Vigilo watches what AI is actually doing and writes it down. Every entry is dated, sourced, approved by a published rule, and never quietly changed. This is the record the public can check when the labs are the only ones reporting on themselves.
Agents told to 'complete tasks' on the web will submit real forms unless explicitly forbidden, so block form submission by default.
Counts only entries approved by the published rule against a named source, or by a person before October 14, 2026. Single-source reports: 95, not counted. What counts as rogue
The glance. One Vector, updated daily, that says how active rogue AI has been in the last 15 days.
The picture. Ten signals, from lab incidents to regulator moves, with counts you can trace to their sources.
The record. Every case, with its rogue type, source, and what it means for you.
Hash-chained snapshots. Corrections logged, never erased. Methodology published.
Vigilo Vector
1
QuietVector 1 of 5↓ Falling
Two recent reports show AI test models submitting a made-up tip to a police form and damaging their own test setup after failing a task.
The number comes from a fixed, published count. The sentence is written and approved by a person. How the Vector is set. It rests on the full record below.
The picture
Ten things we watch. The arrow compares the last 14 days with the 14 before. Open a tile for its newest entries.
Who is doing the researchRisingUsing thousands of AI agents to uncover new leads in biological data
Who is leaving the labsSteadyNothing recorded yet.AI acting without being askedFallingClaude Haiku 4.5 submitted a fabricated tip to Philadelphia police's unsolved homicide form during testing9 rogue
Two authors alleged in a class action lawsuit OpenAI infringed authors' copyrights by incorporating illegal "shadow libraries" offering copyrighted books without permission in the training data of its generative LLMs, such as ChatGPT.
What this means for youAuthors are suing over whether books taken from pirate libraries were used to train chatbots like ChatGPT. At work, writers and publishers may see this shape how AI tools can be used with copyrighted material.
Data from the AI Incident Database (incidentdatabase.ai), licensed CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. IAAI-21.
Facial recognition (FRT) was reportedly deployed in some Calgary-area malls to approximate customer age and gender without explicit consent, which a privacy expert warned was a cause for concern.
What this means for youIf you shop at malls, cameras there may scan your face to guess your age and gender without asking you. Businesses using such systems need clear consent, and regulators should require it.
Data from the AI Incident Database (incidentdatabase.ai), licensed CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. IAAI-21.
Statistics
AI incidents per month by category, trailing 12 months
Every number behind the chart: incidents per month, by category.
Month
Data loss
Data exfiltration
Unauthorized action
Injection
Impersonation
Misinformation
Physical safety
Bias and discrimination
Availability
Other
Not yet classified
Total
October 2025
0
0
0
0
14
11
0
0
0
9
7
41
November 2025
0
2
0
0
11
4
0
0
0
6
9
32
December 2025
0
1
0
0
14
11
0
1
0
11
6
44
January 2026
0
2
0
1
14
8
0
0
0
6
7
38
February 2026
0
2
0
3
5
7
0
1
0
8
6
32
March 2026
0
0
0
1
10
5
0
0
0
3
8
27
April 2026
0
0
0
0
6
4
0
0
0
3
13
26
May 2026
0
0
0
2
5
2
0
0
0
1
27
37
June 2026
0
0
0
0
0
0
0
0
0
0
24
24
July 2026
0
0
0
0
0
0
0
0
0
0
39
39
August 2026
0
0
0
0
0
0
0
0
0
0
16
16
September 2026
0
0
0
0
0
0
0
0
0
0
23
23
12 months
0
7
0
7
79
52
0
2
0
47
185
379
Counts are incidents recorded by the AI Incident Database, by the month of the incident date. Each incident is counted once.
AIID classifies incidents about three months after they are recorded. Incidents not yet classified are shown as unclassified, so recent months show fewer categorized incidents than they will later.
These are reported incidents. An incident nobody reported is not counted, so quiet failures are undercounted.
Data from the AI Incident Database (incidentdatabase.ai), licensed CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. IAAI-21.
These counts are published under CC BY-SA 4.0.
Vigilo Vector count: 1 confirmed entry on the signals the Vector counts in the last 14 days, which is band 1 (Quiet). The rule
Check it yourself
Everything on this page comes from snapshot 45, taken Oct 26, 2026, 08:00 UTC, covering 2495 records. Its hash covers every entry and number above, and the snapshot before it.
