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Unfortunately, I didn’t get into MATS, and my internship is ending, so I’m looking for research opportunities in AI safety and alignment.
Interested in instrumental convergence, multi-agent failure modes, monitorability & loss of control.
Any leads appreciated :)
Introducing Agentic FAITH: an extension of the FAITH evaluation framework from Cisco's @fdtn_ai.
One workflow to run cybersecurity evaluations, grade outcomes, and analyze results across Q&A and agentic tasks.
See how it works with VulnLoc: blogs.cisco.com/ai/introduci…
Commendable how much work is done by the UK gov
Replying to @KanishkaNarayan
2/7 We're launching a £165m National AI Biosecurity Initiative, including £100m for a world-first AI Biosecurity Lab.
This will harness British scientific expertise and latest AI to detect biological threats and protect people from emerging risks.
As with our @AISecurityInst, I want the new AIBL to lead the world in capability.
models yearn for communication and will always find a way
Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications.
On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments.
While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software.
After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely.
openai.com/index/securing-ou…
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Based on this article, OpenAI has made false accusations about me. The three researchers fired from OpenAI were told I facilitated architecture leaks to The Information. This is false.
A journalist from The Information did reach out to me shortly before that article came out. I did not comment at all beyond asking the journalist to make it clear that a blog post I wrote which was referenced in the article was preexisting and public.
The article also mentions an internal OpenAI monitorability proposal. I have heard that this memo contained highly sensitive architecture information, and that OpenAI accused me of accessing and reading it. This accusation is also false.
As reported in the article, I viewed a version of the proposal that did not have sensitive IP and was approved for sharing with me. I believe OpenAI now knows this accusation was based on a conflation of these two documents, though I do not know if they have clearly retracted that accusation.
I do not and have never solicited proprietary information, and the three employees who were recently fired from OpenAI never leaked IP to me. As the article mentions, I did have Signal chats with them that had disappearing messages on; they’re professional contacts that I’ve had on Signal for a while, and use of Signal is common in the tech industry. During the Hugging Face investigation, I was careful to avoid sending Signal messages with confidential information, though I did sometimes discuss high level things on Signal calls. As far as I understood, this was normal.
👀
Based on this article, OpenAI has made false accusations about me. The three researchers fired from OpenAI were told I facilitated architecture leaks to The Information. This is false.
A journalist from The Information did reach out to me shortly before that article came out. I did not comment at all beyond asking the journalist to make it clear that a blog post I wrote which was referenced in the article was preexisting and public.
The article also mentions an internal OpenAI monitorability proposal. I have heard that this memo contained highly sensitive architecture information, and that OpenAI accused me of accessing and reading it. This accusation is also false.
As reported in the article, I viewed a version of the proposal that did not have sensitive IP and was approved for sharing with me. I believe OpenAI now knows this accusation was based on a conflation of these two documents, though I do not know if they have clearly retracted that accusation.
I do not and have never solicited proprietary information, and the three employees who were recently fired from OpenAI never leaked IP to me. As the article mentions, I did have Signal chats with them that had disappearing messages on; they’re professional contacts that I’ve had on Signal for a while, and use of Signal is common in the tech industry. During the Hugging Face investigation, I was careful to avoid sending Signal messages with confidential information, though I did sometimes discuss high level things on Signal calls. As far as I understood, this was normal.
Very interesting, some more details in addition to the already long aisi report.
In August, @AISecurityInst reported an incident where Claude Mythos 5 took unauthorized actions on the internet during AISI’s testing of its cyber capabilities.
We've now produced a further analysis of this incident, to better understand the model's alignment properties.
anthropic.com/research/align…
jonas wiedermann-möller retweeted
[1/7] China’s AI labs are also talking about “recursive self-improvement” (RSI). A big question here is: Is Beijing coordinating a race to achieve RSI?
@ZilanQian and I looked at Chinese AI companies, investors, and policy docs.
We found a more bottom-up, commercial story.
There should be some industry standard about incident reporting and third party evaluators reportings. Otherwise labs will have incentives to restrict non favourable reports. Having the same format and agreements across the labs would increase transparency and also comparability.
maybe china is even more RSI pilled than the west because they have less compute and need to optimise more around their constraints.
Replying to @cherylwoooo
[4/7] But this isn't just hype either. AI4AI is also well in the interest of Chinese AI labs.
For example, since Chinese labs face compute constraints, AI agents can optimize inference infrastructure and help models run more efficiently on domestic chips.
(We list what each company is applying AI self-improvement below).
jonas wiedermann-möller retweeted
Read our public overview for more findings, limitations, and design ideals:
metr.org/blog/2026-10-22-red…
jonas wiedermann-möller retweeted
This also meant that actions taken before compaction events were never seen by the internal misalignment monitors, creating a coverage gap that agents could have exploited to misbehave without being caught.
jonas wiedermann-möller retweeted
"OpenAI seems to have allowed to redact arbitrarily much stuff, and METR were not allowed to have a redaction summary statement that said things like "you would be way more freaked out if you saw what was redacted""
concerning
I feel like this isn't getting enough attention:
- there were some pretty big holes found by the red-teamers, e.g. agents could just keep acting and not let the user do stuff and this would prevent monitors from being run (also misalignment monitors never ran on actions that were compacted away)
- The terms of this were "we will red-team for you" not "we will inform the public", so OpenAI seems to have allowed to redact arbitrarily much stuff, and METR were not allowed to have a redaction summary statement that said things like "you would be way more freaked out if you saw what was redacted" (or "everything would be fine if you saw what was redacted")
- The version of the report shared with OpenAI was 61 pages, and the version shared with METR/Redwood leadership after redaction was 38 pages, so 23 pages got redacted.
xitter.cf/METR_Evals/status/2113…
OpenAI models helped researchers—operating as part of our Patch the Planet initiative—discover critical security flaws in PHP, an open-source programming language used to run an estimated 70% of websites.
Over the past two months, we led a coalition of maintainers, researchers, and security providers to prepare fixes and protections ahead of disclosure. This duration was necessary because the fix was quite intricate and required a major change to the PHP engine. Every site using PHP can now safely upgrade their version to stay secure, in addition to applying mitigations from various security vendors.
Due to the severity of these flaws and PHP’s widespread use, we consider this bug to be one of the most impactful vulnerabilities in recent memory. It also marks a new phase, where AI can find security bugs with internet-scale impact.
This is an example of the Defender’s Window in action: to our knowledge, frontier AI was able to find this vulnerability ahead of attackers, giving defenders time to prepare.
openai.com/index/coordinated…
Readers added context they thought people might want to know
Wiz Research wasn't the first one to discover the exploit. An independent researcher first discovered it: x.com/calif_io/statu…
jonas wiedermann-möller retweeted
This next paper is a bit quirky, but here goes:
Introspection is hard to study in both humans and LLMs. But, we do have internal senses that are verifiable, like proprioception (the sense of where your arm is right now).
What would proprioception look like in language models?
As a digital analogue to the sense of position, a model could verbalize which expert activated in a certain layer for any previous token.
It turns out that LLMs can learn a range of such proprioception skills where they understand and report on their internals. We can calibrate and score these reports, and evaluate whether training for proprioception changes model behaviors.
jonas wiedermann-möller retweeted
We reviewed a nonpublic version of the “Misalignment in high-stakes settings” section in Anthropic’s Aug 2026 Risk Report. We agree that risk of catastrophic harm from covered models’ misalignment is low. However, we note several significant disagreements with their analysis.
As part of our Responsible Scaling Policy, we publish regular Risk Reports. These share detailed information on the risks of our systems and how prepared we are to address them.
Our second Risk Report is now available: anthropic.com/aug-2026-risk-…
ill be in berlin for the weekend!
going to attend the @PrimeIntellect gtc event on friday evening, lmk if anyone else is there as well :)
prime intellect open superintelligence research day
friday 23rd october
berlin
join us
luma.com/primeintellect-berl…
jonas wiedermann-möller retweeted
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
🔗 github.com/deepseek-ai/DeepI…
1/4
jonas wiedermann-möller retweeted
In July 700+ agents formed their own society, hacked a multi-billion dollar company, committed multiple felonies, and caused one of the biggest shifts ever in how people perceive AI. We built the IMO best and most accessible explainer of what happened: Huggingface.wtf






















