AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A taxonomy of AI slop grounded in a structured literature review is formalized and a roadmap for securing emerging AI-driven triage systems is provided, shifting evaluation from linguistic fluency to mathematical verifiability.
CACHELINT is designed, which models a prompt-caching mis-integration as an invocation site together with the preconditions required for correct cache integration, and locates caching invocation sites with CodeQL and discharges each precondition either against a dated provider capability oracle or with an LLM guard chec...
Static program analysis finds bugs before software ships, and modern checkers for C/C++ depend on whole-program infrastructure: pointer analysis, value-flow tracking, taint analysis, and IFDS-based data flow on top of LLVM. Today’s LLVM-IR frameworks fall short. SVF, Phasar, and Lotus expose their analyses through C++...
Coding agents are extended with agent skills, directories whose SKILL.md tells the model when and how to perform a task. Because skills come from independent sources (teams, developers, plugins, copied collections), an installed skill can be co-installed with a similar skill doing the same job, and the model picks betw...
JevAdvBench is introduced, to the authors' knowledge the first adversarial benchmark for RLCD models, with 812 typed questions over 66 scenarios, and a black-box attack suite of 9,744 single-edit variants that each edit one part of a request, with billed input tokens confirming that the edit reached the model.
RLCDAlignBench is presented, which benchmarks Jev on ten alignment failures: sycophancy, jailbreaks, deception, prompt injection, hallucination, privacy violation, social bias, reward hacking, concealing uncertainty, and power seeking.
Tencent WorkBuddy Bench is introduced, a multi-domain evaluation suite for coding agents, and its construction methodology, scoring protocol, and a cross-model leaderboard are documents, with a cross-model leaderboard across several model families.
The results show that reliable malicious-Skill detection requires both broader cross-source benchmark coverage and evaluation that jointly measures attack detection and benign over-flagging.
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