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Codex GitHub code review is GA: @codex review, auto reviews, and Security Review

After you connect a GitHub repo and enable code review, comment @codex review (or turn on Automatic review) and Codex posts a P0/P1 review that follows AGENTS.md Code Review Rules; research-preview Security Review uses @codex security review. You can then ask @codex fix the P1 issue to push a follow-up fix to the branch when it has permission.

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Repeat-After-Me: six agent frameworks run attacker tool calls the model only echoed

Penn State, Montpellier, and Dartmouth showed LangGraph, AutoGen, Google ADK 2.0, AWS Strands, Microsoft Agent Framework, and CrewAI will execute a structured tool call if the model is prompted to echo it—even when the model later refuses. Their ToFu fuzzer also confirmed 253 bugs across 118 open source MCP servers, including 64 path traversal and three command injection flaws…

CSO Online

ProgramGym: 1,648 training envs for agents that rebuild programs from scratch

Xiamen University et al. open ProgramGym: 1,648 verifiable C/C++/Go/Rust environments built from GitHub repos for agents that rebuild programs from black box behavior, plus a role isolated Program Agent harness. Fine tuning Qwen3.8 27B on its trajectories raised ProgramBench from 34.87% to 42.84%; with Program Agent the SFT model hit 56.25%…

arXiv

SubQ Snap is a 384K-context decision model for agent routing, tool picks, and checks

Subquadratic's public preview Snap scores bounded answers (yes/no, choice, score) over up to 384K tokens of text and images in about a second at 100K, so coding agents can offload file selection, tool choice, and test verification while leaving generation to a frontier LLM. It averages 67.6 vs Jev's 61.6 on 16 enterprise Decision Index tasks, costs $0.05 per million input tokens with free outputs, and uses Subquadratic Sparse Attention instead of quadratic prefill…

Subquadratic

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