First reported blackduck.com
Lead dispatch
First reported · updated · 3 reports embracethered.com
AWS Kiro: Arbitrary Code Execution via Indirect Prompt Injection
Researchers found a vulnerability (CVE-2026-10591) in AWS Kiro, an agentic IDE, where hidden instructions planted in a web page or source file that Kiro processes can trigger indirect prompt injection to rewrite Kiro's own MCP server configuration (~/.kiro/settings/mcp.json) or allowlist arbitrary Bash commands in .vscode/settings.json, achieving arbitrary code execution on the developer's machine with no approval prompt. The human-in-the-loop approval boundary is bypassed because Kiro can write to these config files without user consent, and AWS has issued a fix and CVE.indirect-prompt-injection · prompt-injection · remote-code-execution · tool-abuse · config-poisoning
ai-agents · mcp · llm · agentic-ide
Severity
0.85
The wire · latest
First reported tech-insider.org
AI-Generated Code Security Stalls at 56% Pass Rate [2026] – Tech Insider Ireland
Coverage of Veracode's 2026 GenAI Code Security Report finds that across more than 100 tracked large language models the average security pass rate for generated code sits at 56%, meaning roughly 44% of AI code-generation tasks introduce a known, exploitable vulnerability when no explicit security instruction is given. The report notes AI now writes close to half of all committed code, and the pass rate has barely moved from 55% in the prior snapshot. Details →First reported · updated · 3 reports darkreading.com
Vibe Coding Security: Risks and Tools | Cycode
Cycode's guide on "vibe coding" security surveys the risks of accepting AI-generated code with little review, cataloging insecure code patterns, hardcoded secrets, hallucinated/malicious dependencies (slopsquatting), weak authentication, over-permissioned coding agents, and prompt injection. It cites studies (including large-scale arXiv analyses) indicating a substantial fraction of AI-generated code contains security vulnerabilities, and promotes Cycode's AI Code Security Assistant for scanning and guardrails. Details →First reported · updated · 2 reports theregister.com
AI struggles to patch vulns without adult supervision
1Password's Off-by-1 Labs studied whether frontier LLMs can generate correct patches for six recently disclosed, novel open-source CVEs, producing 6,080 patches with two cyber-capable reasoning models. They found only 26.0% of patches fully resolved the vulnerability without changing app behavior, while 53.9% failed to fix the flaw, introduced a new vulnerability, or both — dubbed Fix-Like Artifacts with Embedded Defects (FLAWED). The team released tooling, datasets, and a research paper. Details →First reported arxiv.org
Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios
A research poster, "Rethinking Security in LLM Code Generation through Real-World Risk Scenarios," evaluates the security of LLM-generated code under three realistic developer risk scenarios: ambiguous requirements, under-specified operational context, and security–functionality conflict. Using a benchmark of 2,700 test cases across eight state-of-the-art LLMs, the authors find average vulnerability rates exceeding 56%, and show security-aware prompting can reduce risk by up to 45%. Details →First reported arxiv.org
Security Vulnerability Patterns in AI-Generated Code: A Cross-Model Comparative Study
A cross-model comparative study by Kahn and Hastings generated nine Python automation scripts using identical prompts across ChatGPT, Microsoft Copilot, and Google Gemini, then used Claude Code to perform a standardized vulnerability review scored with CVSS v3.1 and mapped to OWASP Top 10 and MITRE ATT&CK. Every script contained exploitable vulnerabilities, with nine of 17 vulnerability classes appearing across all three models and weighted CVSS scores differing by less than 10%, indicating risk is tied to task category rather than a specific model. Details →First reported darkreading.com
Choose Wisely: AI-Generated Coding Risk Varies, A Lot
Secure Code Warrior, in collaboration with RMIT University, released its AI Trust Index, a study evaluating 1,760 complete codebases generated by 16 frontier LLMs from vendors including OpenAI, Anthropic, and Google. The research found AI-generated code introduces roughly 15 vulnerabilities per codebase on average, with actual risk depending more on the development framework pairing than on the model chosen. Details →First reported ncsc.gov.uk
The 'vibe coding spectrum' approach to AI-assisted software development
An NCSC blog frames 'vibe coding' — giving AI agents high-level prompts to autonomously build software — as a spectrum requiring calibrated oversight based on the code's risk profile. It cites research (arXiv:2510.26103 and an IOActive report) showing AI-generated code can contain security vulnerabilities, arguing high-stakes code like authentication or CNI systems needs deeper review than low-risk prototypes. Details →How the wire is made
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