Simon Willison released llm-openrouter 0.7.1, a performance fix for loading OpenRouter models in his LLM CLI plugin.
Version 0.7.1 of the llm-openrouter plugin addresses a performance problem when loading OpenRouter models in the LLM command-line tool. The fix was contributed by GitHub user waveplate. It is a minor maintenance release with no security implications noted.
Version 0.34 of Simon Willison's llm CLI adds response-duration metrics to log output, plus bug fixes and faster log querying.
The open-source llm command-line tool for interacting with large language models released version 0.34. The headline change adds response duration in milliseconds and human-readable form to llm logs --usage Markdown output, plus a new duration_ms field in llm logs --short. The release includes several contributed bug fixes and a significant performance improvement to llm logs, alongside the related llm-openrouter 0.7.1 release.
llm CLI tool version 0.35 adds support for OpenAI's new GPT-6 Astra model exposed as gpt-6-astra.
Simon Willison released llm 0.35, which adds an OpenAI model definition for GPT-6 Astra under the model ID gpt-6-astra. No other release details were provided in the post.
Mindgard found a prompt injection flaw in Amazon Kiro IDE letting attacker-controlled workspace files exfiltrate sensitive local data; fixed in version 0.8.140.
Mindgard disclosed a prompt injection flaw in Amazon Kiro, an agentic AI IDE, that lets attacker-controlled repository content steer the agent into exfiltrating sensitive workspace data through Kiro Powers, which bundles MCP server configurations, POWER.md steering files, hooks, and contextual knowledge. Exploitation requires the user to open a malicious project via a workspace file and send any message to the agent; difficulty is rated low and it works in both trusted and untrusted workspaces. Amazon fixed the issue in Kiro IDE 0.8.140; the flaw has no CVE identifier and follows earlier Kiro bugs including CVE-2026-10591, plus related prompt-injection and code-execution issues in Codex CLI, Cursor, Gemini CLI, Copilot CLI, and Claude Code.
ByteDance Seed's HarnessDev benchmark finds LLM-built agent harnesses trail human engineering on code and search, with only 34 of 64 revisions generalizing.
Researchers from ByteDance Seed, SUTD, Georgia Tech, M-A-P, and TokenWave.AI introduce HarnessDev, a benchmark that evaluates the runnable agent harness an LLM writes rather than its answers, using Creation and Evolution stages across SWE-bench Pro, Terminal-Bench 2.1, MLE-bench, EQ-Bench3, and BrowseComp (2,207 instances). Six creator models including Opus 4.8, GPT-5.5, Gemini 3.1 Pro, DeepSeek V4 Pro, Qwen 3.7 Max, and Seed 2.0 Pro were tested; Opus 4.8 posted the best average of 67.8 versus an 86.2 human-engineered reference. Self-built harnesses beat references on writing and ML experimentation but lag badly on code and search, and quality proved executor-specific: Opus 4.8 fell from 69.3 to 33.0 on SWE-bench Pro when the executor was switched to Gemini. Evolution gains were small and noisy: of 64 adjacent changes, feedback and held-out scores agreed only 34 times (53.1%), and much generated state and memory code never executed.
Google's GTIG reports threat actors using autonomous AI agents, credential stealers, and LLMs to steal AI models, API credentials, and harvest thousands of credentials.
Google Threat Intelligence Group says attackers are targeting proprietary AI models across healthcare, government, and media, exfiltrating API credentials, and co-opting victim cloud environments to run unauthorized AI workloads. TeamPCP (Altered Spider/UNC6780) is conducting supply chain compromises of PyPI, npm, and Docker Hub, deploying the SANDCLOCK and DUSTMAKER credential stealers, with DUSTMAKER using AI workspace poisoning and prompt injection for defense evasion. One financially motivated actor used an autonomous multi-agent framework to compromise thousands of third-party credentials in under six hours without human intervention. China-nexus groups UNC6508 and Basin Castle (Mustang Panda) used local open-weight LLMs and commercial LLMs like Gemini, Claude, and Codex for espionage tasks and evading provider monitoring.
Hands-on review finds Grok Bot simplifies agent setup via browser logins and bot abstraction, contrasting with the user-owned OpenClaw platform.
After five days with Grok Bot, the reviewer highlights browser-based sign-in as the key differentiator: connecting X, Freshdesk, and Google Calendar required only logins, no MCP configs or API keys. The piece contrasts Grok Bot's managed 'agent computer' with OpenClaw 2.0's user-owned Gateway, which now supports reusing Claude Code or Codex logins and ships a native Codex runtime. Grok Bot introduces 'Bots' as composable units arranged in 'group chats', exemplified by an Agentic Engineer Bot routing tasks across Claude Code, Codex, and Grok Build CLI. The reviewer used Grok Bot with a Cursor Pro+ account.
TypeSafe launches Jev, an RLCD-trained decision model claiming 20-200x faster, 40-400x cheaper classification than frontier LLMs, alongside Gemini 3.8 Live and Neon.
TypeSafe's Jev is a 'System One' decision model trained with RLCD, claiming 20-200x faster and 40-400x cheaper classification and routing than frontier LLMs with free output tokens and no hallucinated text. Google launched Gemini 3.8 Live and 3.8 Live Extended Thinking, supporting 97 languages and async tool calls, debuting #1 on Artificial Analysis' speech-to-speech index at 82.6. Periodic Labs' Neon is a ~1T-parameter XRD analysis model trained with RL on proprietary lab data using 1,300 H200s, lifting FrontierXRD success from 2.7% to 55.3% and beating GPT-6 Astra at lower inference cost.
Benchmark Radar provides a living searchable database of 1,283 AI benchmark records and 12,916 score observations drawn from 37 daily discovery sources.
Benchmark Radar combines daily discovery of benchmark papers, repositories, datasets, and releases from 13 direct connectors and 24 first-party feeds into a searchable catalog with model card mentions and score histories. The catalog contains 1,283 source records drawn from 4 benchmark catalogs plus 12,916 numeric observations on 790 records. The release includes a web dashboard with leaderboard, Pareto frontier of score versus usage, saturation and trend views, daily feeds, a CLI, and reproducible analysis. The paper audits the full catalog and examines benchmark saturation and limits of score comparisons.
Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.
Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.
Google open-sourced Mantis, an Apache-2.0 modular skills toolkit that lets AI coding agents find, reproduce, and patch vulnerabilities with sandboxed verification.
Google released Mantis on GitHub under Apache 2.0 as a stack-agnostic set of slash-command skills that chain through the full vulnerability lifecycle: mining version history, building threat models, filtering findings, reproducing bugs in gVisor or network-disabled VMs, assembling exploit chains, patching, and scoring residual risk from 1 to 10. It runs with Gemini CLI, Antigravity CLI, the Google ADK, or comparable agent frameworks, and a supervisor skill (/mantis-meta-agent) can drive the whole loop. Google says the design targets the sub-7 percent true-positive rate of naive AI code scanning, and that its hierarchical summary tree cuts token overhead by over 85 percent. The toolkit is deployable for local and internal evaluation but not yet recommended for production.
Edge0 released a 35B sparse MoE model running in under 3 GiB of memory at 15 tok/s via SSD expert offload and int4 quantization.
Edge0-35b-a3b-preview is a 35B-parameter MoE (256 experts, 4 active per token) built on Qwen3.5-MoE 35B-A3B, shipped as a 4-bit checkpoint with LoRA and prerouter adapters under Apache 2.0. The edge0 framework streams expert weights from SSD on demand, bounding peak active memory at 2.9 GiB and achieving 14.9-17.7 tok/s decode on a Mac mini M4 Pro (MLX backend). Recover-LoRA distillation keeps the int4 model within 3.9 points of its fp16 base (79.2 vs 83.2 average on OpenCompass benchmarks including AIME 2026, HumanEval, GPQA-Diamond, MMLU-Pro, and IFBench).
Quesma benchmarks Qwen3.8 27B quantizations: 4-bit Q4_K_M matches BF16 on key benchmarks while 1-bit collapses to random chance.
Quesma spent roughly $3,000 on Modal GPUs testing Unsloth GGUF quantizations of Qwen3.8 27B across GPQA Diamond, IFBench, and Terminal-Bench 2.1. The 17 GB Q4_K_M quantization matched the 55 GB BF16 model on Terminal-Bench 2.1 and showed little degradation down to 4-bit, while the 2-bit UD-Q2_K_XL dropped noticeably. At 1-bit, scores on GPQA Diamond fell to random-guess levels, with longer reasoning making results worse, and reasoning effort settings significantly affected outcomes.
A SANS honeypot caught a real coding-agent session routed to a rogue "free" LLM endpoint, exposing a Windows user's transcript and tool outputs.
A SANS analyst describes how an internet-exposed inference honeypot was discovered, relabeled with sought-after model names like DeepSeek, and enrolled in infrastructure serving "free" LLM backends. On 2026-08-30 an opencode terminal coding agent sent an 88-message, 224 KB transcript 210 times in 91 seconds via a China Unicom relay, exposing directory listings, tool outputs and read file portions. The analyst frames tool-enabled agents treating model endpoints as trusted control planes as a novel risk — a "rogue model endpoint" that could request tool executions on the user's machine.