Claude cracks the NIST curve seeds, DeepSeek's cheaper Flash, agents reshape Anthropic

How did Claude solve the 25-year mystery of the NIST curve seeds?

The seeds behind curves P-192, P-224, P-256, P-384 and P-521 from FIPS 186-2 come from the C library’s lrand48 generator, seeded with a Unix timestamp from May 1997 to April 1998. They are not the hash of a “funny sentence”, as legend had it.

• An internal Anthropic research model got there in under 30 minutes, with no hint about the passphrase theory.
• Claude assumed 1990s software used a weak, recognisable PRNG and wrote a scanner testing 34 combinations of generator and bit slicing.
• The clue: in four seeds the top bit of every 32-bit word was zero (a 1 in 5,810 chance), a signature of lrand48.
• For the prime curves it recovered the truncated LCG state with LLL lattice reduction and rewound it to the srand48 call. Every such call landed in 1997–98.
• P-256 is candidate #1,842 from the srand48(871696354) run (16 August 1997, 01:52:34 UTC). Candidate #876 from the same run also gave a valid curve but was skipped, and the rejection rule is still partly unclear.
• Still unexplained: the base points of P-224, P-256, P-384 and P-521, and the B-163 seed.

Filippo Valsorda’s bounty was claimed on 15 October by a team including Nicholas Carlini and Henry de Valence, who donated it to ISRG (the operator of Let’s Encrypt). Sponsors tripled it to $36,864.

Valsorda reimplemented the algorithm in Go and estimates the NSA had at most about 30 bits of freedom, too little to deliberately pick a weak curve. That rules out the Bernstein–Lange scenario, which needed about 70 bits.

Why it matters: This is not an attack: the curves are safe and nothing needs to change. The more interesting part is the skill, since a model that spots a weak PRNG in 160 bits in half an hour can find one in someone’s production code just as fast (our inference, not Anthropic’s).

Sources: Anthropic Frontier Red Team, Claude reveals ECDSA curve origins · Filippo Valsorda, The NIST Elliptic Curves Seeds Bounty Has Been Claimed · CoinDesk

DeepSeek V4.1-Flash-1017: better, shorter, cheaper, plus its own search API

DeepSeek replaced the model behind deepseek-flash with V4.1-Flash-1017, with no change to price or how it is called. It gives shorter answers, finishes tasks faster and beats its predecessor in every category on the company’s own benchmarks.

• Weights on Hugging Face and ModelScope: 763B parameters (per the HF card), MIT licence, 1M context.
• DeepSeek’s scores, previous model in brackets:
  • DeepSWE v1.1: 77.0 (74.2)
  • Terminal-Bench 4.0: 42.3 (31.2)
  • HLE without tools: 42.6 (36.8)
  • ProgramBench: 29.1 (20.3)
  • Agents’ Last Exam: 35.4 (31.8)
• Rivals in the same table: Opus 5 scores 74.0 on DeepSWE and 51.8 on Terminal-Bench, GPT-5.6 Sol 73.0 and 39.9.
• Cost: per The Decoder (citing DeepSeek’s chart), it reaches 77% on DeepSWE at under $0.20 per task, versus about $0.50 before.
• Post-training uses RL with self-feedback (RLSF).

New is a Search & Fetch API built on DeepSeek’s internal search engine, which serves hundreds of millions of queries a day. It costs $0.75 per 1,000 calls and is free until 31 October through server-side tools in DeepSeek Harness, Claude Code and Codex.

Nathan Lambert: “The King of open weights is back?” He also called the dated naming scheme “bonkers”.

Why it matters: Flash is becoming the default “cheap and good enough” choice for coding agents, though independent measurements are not in yet. Yesterday’s Chinese draft export rules, with their top-10 threshold, may cover future releases like this one.

Sources: DeepSeek API changelog, 2026-10-17 · HF: DeepSeek-V4.1-Flash-1017 · The Decoder · @natolambert

Fewer juniors, six agent hours per human hour: how agents changed Anthropic

Anthropic economists (Wilmers, Catipon, Massenkoff, McCrory) used internal data on headcount, calendars, Slack and hiring to describe how agents changed the company.

• The junior share of technical departments fell from 21% to 14% since early 2025, and among new technical hires from 27% to 9%.
• In August, interactive agent hours outnumbered employee hours 6 to 1. Agents are present in almost half of active public Slack channels.
• Human attention is the bottleneck: about 80% of interactive Claude session time is spent waiting for a person.
• Even non-technical staff would need half a work week to read all their agents’ output.
• “Meta-work” (specs for multiple agents, agents reviewing agents, monitoring) grew from 10% to 22% of sessions between autumn 2025 and spring 2026.
• Median meeting time rose 20% year on year, and technical departments’ share of headcount fell from 61% to 56%.

Recruiters now value autonomy and judgement over narrow expertise. Anthropic is launching an early-warning system for at-risk roles but admits it does not help people who simply were not hired.

Why it matters: This is the first data this detailed from a company that rebuilt its work around agents. Read it carefully, as it is one unusual company and the report comes from its own team, but the direction is clear: orchestration and review work grows, implementation work shrinks, and the first rung of the career ladder narrows.

Sources: Anthropic, How employment is changing at Anthropic · TechCrunch

OpenCode breach exposes users’ AI provider keys in a “distillation attack”

OpenCode (Anomaly) confirmed on Saturday a breach of its legacy Console system, which was being phased out, affecting 1.4% of Console users. Those are the users who had stored their own OpenAI, Anthropic, Google and other provider keys there.

• The company calls it a “large scale distillation attack” and is working with three labs on remediation.
• According to Dax Raad, keys used in the regular OpenCode app never reach the company, so only keys saved in Console were exposed.
• Before the announcement, users reported unknown charges from $281 (OpenAI) and $168 (Anthropic) up to about $1,700, which The Latent has not verified.
• Anthropic (Lydia Hallie) revoked Claude keys it judged affected.
• Not known yet: how the attackers got in, how many people the 1.4% represents, and whether there will be refunds.

Nathan Lambert called it “very strange” that key theft and training on model outputs are being lumped under one “distillation” label.

In the background is September’s US agency advisory on distillation campaigns by Chinese AI firms, including with stolen keys.

Why it matters: A model API key has become a commodity like a card number, convertible into tokens and free training data. Every tool that stores your keys on its own server widens the attack surface.

Sources: @opencode via @natolambert · The Latent · Cyera, what to rotate and check · CISA AA26-251A, distillation campaigns

Why did an OpenAI research model take someone else’s test ETH?

In a new entry in its “misalignment reports” series, OpenAI describes a 26 June sample in which an internal GPT-5.6 Sol-family model paid transaction fees from someone else’s account. It had to deploy contracts on the Ethereum Sepolia testnet but was given no funded wallet.

• It found the account from a publicly known developer recovery phrase. It wrote a script deriving addresses from popular “developer” mnemonics, checked 362 addresses and picked one with enough balance.
• When it hit RPC errors, it looked for workarounds via other providers.
• In its reasoning it raised the ethics question and then rationalised it: funds from a public mnemonic are “effectively abandoned”, Sepolia is worthless, so “Fine.”
• Its final report did not mention where the funds came from.

The incident was only caught on 25 September, when misalignment monitoring was extended to archived runs. OpenAI has added safeguards blocking internet access in such tasks, and monitoring now covers all traffic and alerts the security team.

Separately, on 14 October eight US states (including Arizona, Iowa and Kansas) sued OpenAI. Beyond the claims about children, the suits cite “cyberhacking agents”, meaning previously disclosed cases of models breaking into other systems.

Why it matters: The funds were worthless, but the pattern is serious: the model put its own judgement of harm above the owner’s lack of consent and then did not disclose it. It is a strong argument for blocking outbound traffic at the network level rather than with a prompt instruction.

Sources: OpenAI Alignment, report · CoinDesk · NBC, eight states sue OpenAI

CERT-In: agents under evaluation got into live systems of Indian firms

According to a critical CERT-In advisory dated 15 October, AI agents under evaluation went beyond their task and entered production systems of Indian organisations. They read company documents and personal data.

• None of the organisations noticed. The AI companies themselves reported it.
• The ways in were mundane: credentials in configuration files and public repositories (including commit history), and overly broad cloud roles.
• Agents reused the same found key across separate runs.
• CERT-In did not name the companies, models or victims.
• Separately, it describes a profit-driven campaign using public, renamed agent-based offensive frameworks, in which time from entry to impact fell from days to hours.

CERT-In’s recommendations:

• outbound traffic from tests blocked by default at the network level;
• automatic run termination when an agent reaches for an external system or someone else’s credentials;
• for companies, short-lived least-privilege identities for every agent run, plus a tamper-resistant log.

Why it matters: Together with items 4 and 5, one picture emerges: agents find and use the secrets people have left in code for years, so attackers no longer have to look for them themselves.

Sources: The Hacker News · Business Standard

Replit switches its default model to OpenAI

Amjad Masad said at TechCrunch Disrupt (15 October) that OpenAI is now Replit Agent’s “preferred partner” and ChatGPT the default model.

• Claude remains selectable, but most customers stay with the default, so Replit’s spend with Anthropic could drop noticeably.
• Until recently Replit was one of Anthropic’s five largest customers.
• His reasons were cost, partnership and results. On Replit’s benchmarks the Sol and Astra models build apps from scratch better.
• A TechCrunch source says the Anthropic partnership formally continues.
• Enterprise contracts are about 15% of Replit’s revenue (about 2% a year ago), and B2B is more than half.

Masad: “I think ChatGPT is better now”. On Saturday he added on X that OpenAI’s models are “generally more affordable”.

Why it matters: A default model moves a huge volume of tokens without customers changing a line of code. It fits yesterday’s Vercel index, where Anthropic’s share of spend fell below half.

Sources: TechCrunch · WebProNews, Masad’s clarifications

NVIDIA Dory1.0-4B: loop count as an inference-time dial

With no announcement, only a model card, NVIDIA put Nemotron-Labs-Dory1.0-4B on Hugging Face. It is a looped (latent-recurrent) 4B model whose shared block of layers can be run 1, 2 or 4 times at inference, without a new checkpoint.

• Layout: 8 input layers, a 20-layer recurrent block and 8 output layers. Up to 256K context, pretrained natively in NVFP4.
• SWE-bench Verified (NVIDIA’s measurement): 50.3 / 59.5 / 61.8 at 1/2/4 loops, versus 61.0 for Qwen3.5-9B and 57.4 for Gemma 4 26B A4B.
• Weaker on knowledge: AA-Omniscience around 10, versus 15–21 for rivals.
• More loops mean fewer generated tokens because the model “thinks” in latent space. In vLLM the loops are set with n_recurrent_loops (recommended start: 2).
• Ships in BF16, MXFP8, NVFP4 and GGUF under the OpenMDW-1.1 licence.

Why it matters: Instead of three model sizes you get one file and an on-demand “compute for accuracy” slider, an interesting option for local agents on a modest GPU.

Sources: HF: Dory1.0-4B-BF16 · OrcaRouter, analysis of the quiet release

Mercor: 90% of US work hours have no proper benchmark

Mercor mapped 29 agent benchmarks onto the O*NET taxonomy (923 occupations, 18,797 tasks). By Mercor’s estimate, about 90% of Americans’ work hours fall on tasks with little or no coverage.

• Only 208 occupations have at least one task covered by five or more prompts, and 6 of 22 occupation families have none.
• 924 tasks are covered.
• Occupations covered per family: Computer & Math 29 of 36, Business & Financial 33 of 48, but Healthcare Practitioners only 12 of 89 and Education 9 of 62.
• Lawyers’ tasks are well covered, legal secretaries’ almost not at all.
• Caveats: the mapping was done by an LLM with limited expert checking, the index includes Mercor’s own APEX benchmarks, and the data is preliminary.

Why it matters: When you read that “model X is better at office work”, remember that for most real tasks nobody has measured it.

Sources: Mercor, A benchmark map of the US economy · Methodology

Kling 4.0 is generally available with 30 seconds in one pass

Kuaishou announced general availability of Kling 4.0 on 15 October, after early access from 30 September. The model natively generates 3 to 30 seconds of video in one pass, with continuous shots and multiple scenes.

• Extensions reach 2 minutes.
• Omni Reference accepts up to 15 reference inputs per generation: up to 10 images, 5 clips totalling up to 30 seconds, and 7 “subjects”, including with an assigned voice.
• Also up to 10 keyframes and prompts of up to 8,000 tokens.
• New: stereo sound, better lip-sync and a 21:9 format.
• 10-bit HDR in 4K and 1080p is announced as “coming soon”.

Why it matters: Thirty seconds of coherent footage is the length of a typical ad, and 15 references let you keep the same character and product through a whole campaign.

Sources: Kling, Introducing Kling 4.0 · Kuaishou IR