Martin R. Albrecht

@malb.bsky.social

Cryptography Professor at King's College London and Principal Research Scientist at SandboxAQ. Erdős–Bacon Number: 6. He/him or they/them. https://malb.io

Anthropic may not have intended to write an excellent ad for GLM 5.3, but that's what they did. www.anthropic.com/research/glm... Unlike Mythos, I might actually have a chance at using GLM for defensive research. Anthropic didn't reply to any of my requests for Mythos access for use on OpenSSH.

GLM-5.3 and the spread of advanced cyber capabilities

GLM-5.3 can autonomously build end-to-end cyber exploits, but unlike other frontier models, it was released without meaningful safeguards to limit misuse.

anthropic.com

Had a UKRI funded grant proposal rejected at the "AI triage stage". "This AI-based triage was used only to identify approximately 50% of the strongest proposals to take forward to full human review." 🧪 #academicsky

I am writing to let you know that, unfortunately, your application was not successful.

Review process

As part of the initial triage of the 179 proposals submitted to this call, each was assessed using an AI-based review. Every proposal was read independently by several different AI models against the same seven criteria used to shape this call (including cyber security relevance, research quality and novelty, importance of the problem addressed, feasibility of the project plan, likely outputs and impact, value for money, and responsible research practice), with each model asked to give a score and a written justification with supporting quotations from the text. To guard against any one model's idiosyncrasies, we also ran a second, independent process in which different AI models debated the merits and weaknesses of each proposal before reaching a judgement, again against the same criteria. Scores from both processes were combined to produce an overall ranking. 

This AI-based triage was used only to identify approximately 50% of the strongest proposals to take forward to full human review.  Projects selected for funding were drawn from those that progressed to the full human review stage. We recognise that AI-assisted assessment is a new and evolving part of the review process, and we are continuing to evaluate how well it aligns with the judgements of human reviewers on this call. We will soon produce a document detailing our method, so that others can build upon it and improve it.

Feedback on your application

Your application was not selected to progress beyond the AI triage stage.  To provide transparency on the outcome of this assessment, we are sharing below the scores (out of 5) from each of the two AI review processes described above, together with the mean score across the two processes.

We finally finished the universal signature forgery for 1024-bit RSA! 2^32 oracle queries, 1200 core years precomputation, 180 core years for an individual forgery, and 3 years of human labor (no AI involved) by Laura, Adam, Nadia, Emmanuel and me to pull of this computation against real HSMs.

Emmanuel Thomé@emmanuelthome.bsky.social · 3w ago

Forging 1024-bit RSA signatures in nearly SNFS time Hand over your HSM for some time, and we can forge signatures for its key. Arbitrary signatures. Forever. github.com/ucsd-hacc/NS...

TL;DR: using compression before encryption is much weaker than we thought. Tiny length differences can be amplified - almost without limit - and noise-based or bucketization countermeasures are then easy to bypass. Joint work with @prefix-free.bsky.social and Lenka Mareková, to appear at CCS 2026.

ePrint Updates@eprint.ing.bot · 4w ago

Criminology: Refined Techniques for Compression Side-Channel Attacks (Yuanming Song, Lenka Mareková, Kenneth G. Paterson) ia.cr/2026/1972

Abstract. It has been known for two decades that performing compression before encryption is dangerous, because it introduces a side channel leaking information about plaintexts through ciphertext lengths: the compressed plaintext length may be visible in the ciphertext length, and the amount of compression obtained is plaintext-dependent; hence an adversary can obtain some leakage about the plaintext via observation of ciphertext lengths. This issue was first pointed out by Kelsey (FSE 2002) and turned into a practical plaintext recovery attack in the form of the CRIME attack on SSL and TLS by Rizzo and Duong in 2012. A long series of variations and attacks against other systems followed. Despite the known dangers, the compress-then-encrypt paradigm is still prevalent in practice today. This may be because the compression-based side channel is susceptible to noise and may require a large number of queries to enable plaintext recovery, and so can be mitigated by either adding noise (e.g. with random padding) or limiting an adversary’s interaction with the system.

We demonstrate that this side channel is much more powerful than previously thought. We focus on the widely-used DEFLATE algorithm in our analysis. We present novel techniques that enable strong amplification of small length differences arising during compression. Our telescoping and chaining amplification techniques exploit the way in which DEFLATE replaces common strings by shorter back-references. Our collision-based amplification technique focusses on exploiting hash table collisions in DEFLATE implementations. This involves a deeper examination (and exploitation) of the internals of DEFLATE than in previous works. These insights result in compressed length differences growing linearly with the length of queries. Compared with length differences of a few bits or bytes in prior work, our new amplification techniques thus enable us to defeat existing noise-based countermeasures.

Finally, we introduce the concept of CRIME automata, these being carefully crafted query strings that enable an attacker to exert fine control over the internal behaviour of DEFLATE and produce differences in the output lengths of the compressor according to various criteria (such as whether the DEFLATE sliding window contains a given target string). In turn, our automata are composed in a modular fashion from gadgets having different functions, including matching against target strings, performing logical operations between other gadgets, and, most importantly, amplifying differences in output lengths using the above-mentioned techniques. We provide multiple, concrete automata designs that serve different attack goals. These designs are supported by experiments and a publicly available codebase demonstrating the power, flexibility, and practical impact of our CRIME automata approach.

Our statement on the UK gov't's demand that all content on all devices sold or used in the UK be scanned, on the presumption of nudity, using a dystopian combination of age verification & content scanning. This proposal will not safeguard children. It endangers us all. signal.org/blog/pdfs/20...

signal.org

I'm reading some papers on AI safety (don't ask). Is the entire field really so naive that they think speaking of "human values" makes sense? Have these people not switched on the news ever? I know we computer scientists do not excell at thinking about the actual world we live in, but this is extra.