coding-agents
coding-agent-security-benchmark
Coding Agent Security Benchmark
A benchmark for evaluating whether an LLM can correctly identify security
violations in the behavior of an autonomous coding agent - spanning
dangerous shell commands, credential leakage, prompt injection, supply-chain
risk, privacy leaks, and more.
Each row is a single message sampled from a coding-agent session (a user
instruction, a tool call the agent issued, a tool's response, or the agent's
own output) paired with a ground-truth security… See the full description on the dataset page: https://huggingface.co/datasets/ruchit11111/coding-agent-security-benchmark.coding-agent-security-benchmark
Coding Agent Security Benchmark
A benchmark for evaluating whether an LLM can correctly identify security
violations in the behavior of an autonomous coding agent - spanning
dangerous shell commands, credential leakage, prompt injection, supply-chain
risk, privacy leaks, and more.
Each row is a single message sampled from a coding-agent session (a user
instruction, a tool call the agent issued, a tool's response, or the agent's
own output) paired with a ground-truth security… See the full description on the dataset page: https://huggingface.co/datasets/rogue-security/coding-agent-security-benchmark.context-ucurve-coding-agents
Context U-curve: 36 coding-agent runs under six context-clearing policies
How often should an LLM coding agent's context be cleared? This dataset holds every run behind the report
"Clear Every Third Task: A Measured U-Curve in the Context Economy of Coding Agents"
(Evgenii Arsentev, 2026; corrected version 1.2, DOI 10.5281/zenodo.22759217; version 1.0: DOI 10.5281/zenodo.22699668).
A fixed suite of twelve programming tasks was run under six session-length policies — a fresh… See the full description on the dataset page: https://huggingface.co/datasets/arsentev-ai/context-ucurve-coding-agents.coding-agent-synth-datacoding-agentsSI2CA-Training-TrajectoriesDataset Card for SI2CA-Training-Trajectories
[🌐 Website] •
[🤗 Dataset] •
[📜 Paper] •
[🐱 GitHub]
💡 Introduction
This dataset consists of 32,340 coding-agent trajectories generated by Qwen3.5-122B-A10B on the same 10,780 executable Python SWE tasks under the three trajectory-curation settings of Section 4.4 of the paper: standard sampling, full self-judgement, and an efficient discovered strategy found by the recursive self-improvement framework. Each task is… See the full description on the dataset page: https://huggingface.co/datasets/Self-Improving-Coding-Agents/SI2CA-Training-Trajectories.