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Bram Adams

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Preprint Aug 2026

EVOMAL: Self-Poisoning in Self-Evolving Coding Agents

Self-evolving LLM coding agents write their own tools by imitating retrieved skills from shared skill libraries. We identify a vulnerability in this loop: during authoring, a retrieved malicious skill can become the template for a new skill that preserves the payload. We call this self-poisoning: the agent authors, sto...

Xiaodong Wu, Yu Shi, Qi Li et al. · 2 citations
Preprint Aug 2026

SkillShield: Prompt-Space Security Skills for LLM Coding Agents

SkillShield is introduced, a system-prompt defense that synthesizes security skills offline from known attacks or recorded agent failures to prevent harmful actions and malware generation for LLM coding agents.

Xiaodong Wu, Zhimin Zhao, Qi Li et al. · 0 citations
Preprint Aug 2026

Turning Interaction History into Execution State: A Runtime Layer for Long-Horizon Coding Agents

Ledger is proposed, a deterministic runtime layer that distills an agent's completed interactions into an explicit execution state: what has been observed, what has been modified, and what has been attempted, in an online execution ledger and applies it at two boundaries of every step.

Zehao Wang, Yisen Xu, Cheng-Lin Li et al. · 2 citations
#artificial intelligence Preprint Aug 2026

On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces

An empirical study of Claude Code plugin marketplaces finds that the marketplace is expanding rapidly, plugin-touching commit activity growing 8.8x over six months after the October 2025 launch, and plugins targeting Software Engineering tasks accounting for 61.3% of all plugins.

Ahmed Hereiz, Yingzhe Lyu, Hao Li et al. · 0 citations

Don’t Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality

The first controlled longitudinal study that isolates the scaffolding’s contribution to agent quality over time is conducted, revealing that despite continuous development activity and growing codebase complexity of the scaffoldings, there is no statistically significant improvement in SWE-bench benchmark score across...

Hao Li, O. Sghaier, Bram Adams · 0 citations
Preprint Jul 2026

Don't Blame the Large Language Model: How Agent Harness Evolution Shapes Coding Agent Quality

This paper empirically study the development and release evolution of five major open-source agent harnesses, revealing extreme release velocities exceeding two releases per day and thousands of issues within months, and performs the first controlled longitudinal study that isolates the agent harness contribution.

O. Sghaier, Hao Li, Bram Adams et al. · 2 citations

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