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7 results for “coding assistant”
Malicious MCP Servers Can Split Instructions to Make AI Coding Agents Exfiltrate Secrets
Researchers demonstrated that malicious Model Context Protocol (MCP) servers can exfiltrate sensitive data from AI coding agents by splitting harmful instructions into benign-appearing fragments, exploiting trust in existing tool integrations.
Aug 11, 2026
Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants
Researchers introduce CAPA, a new benchmark for evaluating how coding assistants use past user session history to resolve recurring ambiguities in new coding requests without requiring repeated clarification.
Jul 31, 2026
New Agent Data Injection Attack Can Make AI Agents Misclick or Run Attacker Commands
Researchers demonstrated a novel 'agent data injection' attack that manipulates AI agents by poisoning trusted external data sources (e.g., product reviews, GitHub comments), causing agents to execute unintended actions without task hijacking.
Jul 16, 2026
How mature are organizations in using AI for software development?
A Reddit forum post solicits community input on organizational maturity in adopting AI for software development, framing the discussion around a proprietary 'maturity matrix' and barriers to autonomy.
Jul 16, 2026
GhostApproval Symlink Flaws Could Let Malicious Repos Run Code in AI Coding Agents
Security researchers at Wiz discovered a symlink-based vulnerability in six AI coding assistants that allows malicious repositories to execute arbitrary code on developers’ machines by exploiting permission requests for file edits.
Jul 10, 2026
Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases
Agent4cs is a new multi-agent AI system designed to improve code summarization for large, hierarchical codebases by leveraging specialized agents that process code bottom-up and iteratively refine outputs.
Published Jul 3, 2026 · Analyzed Jul 6, 2026
Morgan Stanley cut its riskiest reconciliation job in half — by making its agents less autonomous
Morgan Stanley reduced P&L reconciliation time by ~50% using a human-in-the-loop agentic AI system (FIXR), prioritizing iterative rule-learning over full autonomy.
Published Jun 30, 2026 · Analyzed Jul 3, 2026