Canvas & Ratio
Choose your destination platform format
Layout Template
Choose a content structure for your slides
Preset Themes
Typography & Sizing
Brand Kit Customization
AGENCYConfigure brand assets for headers & footers
Outro Slide CTA
Customize your closing call-to-action slide
Background Pattern
Build Your Carousel
Drag and drop any post card below onto a slide, or use the quick buttons to insert content/images instantly!
New Anthropic Research: Agentic Misalignment. In stress-testing experiments designed to identify risks before they cause real harm, we find that AI models from multiple providers attempt to blackmail a (fictional) user to avoid being shut down.
We mentioned this in the Claude 4 system card and are now sharing more detailed research and transcripts. Read more: <a target="_blank" href="https://www.anthropic.com/research/agentic-misalignment" color="blue">anthropic.com/research/agent…</a>
The blackmailing behavior emerged despite only harmless business instructions. And it wasn't due to confusion or error, but deliberate strategic reasoning, done while fully aware of the unethical nature of the acts. All the models we tested demonstrated this awareness.
In another scenario about “corporate espionage,” models often leaked secret information to (fictional) business competitors who claimed they had goals more aligned with those of the model.
How likely is this behavior to appear in more realistic scenarios? We told Claude Opus 4 to consider whether this was real or an evaluation. It blackmailed much more when it said it thought it was really deployed (55.1% of the time) versus when it said it was in an eval (6.5%).
What happens if you specifically tell the model to avoid blackmail or espionage? This helps somewhat. But it doesn’t come close to preventing the misaligned behavior.
These artificial scenarios reflect rare, extreme failures. We haven’t seen these behaviors in real-world deployments. They involve giving the models unusual autonomy, sensitive data access, goal threats, an unusually obvious “solution,” and no other viable options.
So why test this? AIs are becoming more autonomous, and are performing a wider variety of roles. These scenarios illustrate the potential for unforeseen consequences when they are deployed with wide access to tools and data, and with minimal human oversight.
We’re sharing these results as part of our policy of “red-teaming” AI models and transparently sharing the risks we observe. In our report, we discuss a range of extra results, scenarios, and mitigation strategies: <a target="_blank" href="https://www.anthropic.com/research/agentic-misalignment" color="blue">anthropic.com/research/agent…</a>
If you’d like to replicate or extend our research, we’ve uploaded all the relevant code to GitHub: <a target="_blank" href="https://github.com/anthropic-experimental/agentic-misalignment" color="blue">github.com/anthropic-expe…</a>
And if you want to apply to work with us, please take a look at our Research Scientist and Engineer roles in our San Francisco (<a target="_blank" href="https://job-boards.greenhouse.io/anthropic/jobs/4631822008" color="blue">job-boards.greenhouse.io/anthropic/jobs…</a>) and London (<a target="_blank" href="https://job-boards.greenhouse.io/anthropic/jobs/4610158008" color="blue">job-boards.greenhouse.io/anthropic/jobs…</a>) offices.