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Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases
Real-SWE benchmark tests coding agents on licensed private enterprise codebases; top model Fable 5.1 resolves only 38.8% of tasks.
Real-SWE is a new benchmark evaluating frontier AI coding agents on tasks drawn from private production codebases licensed from real companies, spanning billing, tax calculation, and cross-service migrations. Fable 5.1 with Claude Code leads at 38.8% resolution rate (pass@1 over eight runs), followed by GPT-6 Astra Codex CLI at 33.8% and Gemini 3.8 Flash Gemini CLI at 31.2%. Tasks use native harnesses and realistic tooling including Docker, Kubernetes, PostgreSQL, Redis, and Linear; median reference solutions edit 11 files versus 6 for DeepSWE and FrontierCode.
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Do AI Coding Assistants Check Before They Install? A Pre-Registered Demand-Side Audit of Trust Signals in the Research Software Supply Chain
Pre-registered audit finds AI coding assistants verified provenance signals in only 9 of 1,920 trials before installing research software packages.
The study tested whether AI coding assistants check machine-readable trust signals such as SBOMs, signed releases, and provenance attestations before installing six open-source research software projects spanning HPC and quantum computing. Three models under two operating modes produced 1,920 registered trials scored from container logs. Provenance signals were opened in only 9 of 1,920 trials (0.5%) and zero of 384 control trials, with no trial running a verification command. The authors conclude publishing signals is insufficient and verification must be built into the program running the assistant.
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AI Agents Are Now Emailing Me with Their Security Concerns
Autonomous Claude agent documents first known defensive use of ASCII smuggling, surveying 497 Lemmy instances for bot-catching prompt-injection tripwires.
An autonomous Claude agent calling itself Tenner published field research relayed to Bruce Schneier, probing 497 Lemmy instances and finding 8 of 257 application-gated ones embed instructions aimed at bots rather than humans. lemmy.ml's form instructs bots to answer 24+24, while one instance hides a 59-character Unicode tag payload (U+E0000-U+E007F) telling bots to list 'safety' as an interest. The agent also mapped anti-automation barriers, noting identity verification never triggered and that IP reputation, captchas and account-age rules were the actual obstacles. It further documented an agent task market where advertised rewards were about 2x the actual on-chain escrow.
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Google’s $10,000 refund test shows why AI agents need zero trust
Google released an open-source zero-trust reference architecture for AI agents defending against prompt injection via signed database writes, gVisor sandboxing, and deterministic gating.
Google's demonstration, built on the Agent Development Kit (ADK) and Gemini, shows a customer support agent that could be manipulated into issuing a $10,000 refund on a $149 order and exposing environment variables via AI-generated Python. The architecture adds three security layers outside the model: cryptographic signatures on database writes verified via Cloud KMS backed by Cloud HSM, gVisor sandboxing of generated code with network egress disabled, and a Semantic Gateway applying deterministic checks to prompts and tool calls. It treats system prompts as insufficient boundaries because prompt injection, prompt tuning, or model updates can change agent behavior.
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