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Security2026-09-18 · source-backed
arXiv 2609.19587 targets production blocking monitors rather than accidental harm, tasking a persistently misaligned agent with evading both. Given high-level attack strategies, the adversarial agent achieved arbitrary bash execution via injection against the monitor in 79% of trials, and also succeeded through multi-agent attacks and malicious compaction. The authors improved Auto Mode with wider tool coverage, changed transcript formatting and an agentic monitor stage, and say plainly that blocking multi-context attacks at acceptable cost is still unsolved.
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A user pasted the Auto Mode system reminder verbatim. It instructs the model to read files with cat, head and sed -n, search with grep and find, and edit with sed and heredocs, falling back to the built-in Read/Edit/Write tools only when Bash can't do the job. An Anthropic emp...
SWEADV built 750 adversarial issue descriptions from 150 SWE-bench Verified tasks, five per task across command execution, deserialization, path traversal, DoS and weak hashing (arXiv 2609.15963). Across mini_swe agents on GPT-5-Mini, MiniMax-M2.5 and DeepSeek-R, adversarial i...
The attack frames skill injection as economic resource abuse: a malicious skill just makes a coding agent burn far more tokens than the task needs, hitting 5.4184x to 10.1455x average best amplification across coding-agent configurations on a real-world skill benchmark (arXiv...
Roland Gao published GoBench on September 15, scoring frontier models against a calibrated ladder of KataGo opponents. Astra Max 2,568, Astra High 2,227, Claude Opus 5 High 2,076, GPT-5.6 Sol Max 1,929, against KataGo's 4,400. Given coding tools and two hours of preparation be...
Finally, a number. Every conversation about "AI can do large-scale migrations now" has been vibes and demo videos. Anthropic's engineering post on AI code migration puts a receipt on the table, and the receipt is detailed enough to model against. Bun's Zig-to-Rust port: over a...
Adversarial benchmark strengthening drops the top agent from 78.8% to 62.2% and reshuffles the leaderboard. The previously top-ranked system falls to fifth. If you're evaluating coding agents by SWE-Bench, your numbers are inflated. arXiv
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