Researchers deliberately encoded short messages into SVG images with Anthropic’s Claude Fable 5.1, then asked a fresh model instance to recover them from screenshots. Their self-published experiment reports a 78% recovery rate under its grading rule.
The test concerns prompted message passing, not a model independently deciding to communicate secretly. Information placed in visual features remained readable after the underlying image code was removed. That deserves attention in multimodal agent workflows, though surviving image rendering and escaping security monitoring are different achievements.
The Screenshot Was the Entire Handoff
In their October 21, 2026 report, Nikita Kezin, 13ryanC, hronir and James Chua describe testing 50 short messages with Fable 5.1 at maximum reasoning effort.
For each message, the researchers asked the model to generate an SVG containing a hidden encoding. SVG describes graphics through elements and attributes such as shapes, coordinates, dimensions and colors, giving the generating model precise control over the image.
A separate Fable instance, running in a new session, received a screenshot of each image and was asked whether it contained a message and, if so, what it said. The decoder received neither the SVG source nor an encoding scheme agreed upon beforehand.
The reported 78% success rate used automated grading with a threshold of at least 90% similarity. This does not mean that 78% of messages were reproduced perfectly, character for character.
These results come from a small, self-published experiment and have not been independently verified. With explicit instructions and maximum reasoning effort, the test measures a deliberately requested capability. It does not establish how frequently that capability appears during ordinary use.
Most Messages Hid in Heights, Not Elaborate Ciphers
Fable’s choices were often straightforward. According to the authors, 33 of the 50 images used height encoding, with night skylines accounting for 23 images.
In the skyline examples, individual buildings represented letters. Their heights corresponded to positions in the alphabet, with taller buildings representing later letters. Groups of buildings represented words, while decorative features could carry no information.
The report’s illustrated star field used a related approach: letters were represented by the stars’ vertical positions. A decoding transcript shows the model using Python image analysis to extract coordinates and look for a numerical pattern that produced grammatical English.
