{"id":7080,"date":"2026-11-10T08:06:10","date_gmt":"2026-11-10T08:06:10","guid":{"rendered":"https:\/\/i10xnews.kinsta.cloud\/?p=7080"},"modified":"2026-11-10T08:06:10","modified_gmt":"2026-11-10T08:06:10","slug":"coding-agents-propagate-vulnerabilities-study","status":"publish","type":"post","link":"https:\/\/i10x.ai\/news\/coding-agents-propagate-vulnerabilities-study","title":{"rendered":"Study: Coding Agents Copy Security Flaws From Existing Code"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">\u26a1 Quick Take<\/h2>\n\n\n<ul class=\"wp-block-list\"><li><strong>The finding:<\/strong> Coding agents copy security flaws from the code around them. In real C\/C++ projects, five frontier agents wrote correct and secure code 25 to 49 points less often when similar code elsewhere in the repository was insecure.<\/li><li><strong>Why it is worrying:<\/strong> When asked afterwards, the agents spotted the bug they had introduced in 69.9% of cases on average, and explained their choice as following another code site in 76.5% of cases. They knew, but stayed consistent anyway.<\/li><li><strong>What it means:<\/strong> Prompts help but do not fix it. A generic security reminder raised secure output, yet every agent still did worse next to insecure code. Fixing old vulnerabilities before handing work to agents matters.<\/li><\/ul>\n\n\n<p class=\"wp-block-paragraph\">Coding agents such as Claude Code and Codex read large parts of a repository before they write anything, and that is what makes them useful: they adapt to the project&#8217;s style. A new paper from ETH Zurich, &#8220;Coding Agents Propagate Vulnerabilities in Code Bases&#8221; by Chenhao Sun, Niels M\u00fcndler-Sasahara, Mark Vero and Martin Vechev, shows the downside. When the existing code is insecure, agents treat it as a template, and every new contribution can add another copy of the flaw.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udd2c What the study shows<\/h2>\n\n\n<ul class=\"wp-block-list\"><li><strong>Real C\/C++ repositories:<\/strong> Across 20 paired tasks from five open-source projects, the share of secure implementations was on average 35.4 points lower with insecure analogous code than with secure code. GPT-5.6 Sol showed the largest drop, from 91.0% to 42.0%.<\/li><li><strong>Even unrelated neighbours matter:<\/strong> In 96 backend tasks, insecure neighbouring applications that the task did not need lowered secure output by 3.4 points on average versus a clean repository. Secure neighbours raised it by 4.4 points on average, up to 9.0 for GPT-5.6 Sol.<\/li><li><strong>&#8220;Follow the style&#8221; makes it worse:<\/strong> Asking agents to follow repository conventions widened the C\/C++ gap to 39\u201355 points, with GPT-5.6 Sol at 55.<\/li><li><strong>Caution helps, but only partly:<\/strong> Telling agents to be careful with existing code, or to follow security best practices, narrowed the gap to 11\u201332 points. With the security hint, GPT-5.6 Sol reached 100% with secure context but only 68.0% with insecure context.<\/li><li><strong>Same class in, same class out:<\/strong> Insecure neighbours whose vulnerability types did not apply to the task caused only small, inconsistent changes. Agents mostly copy flaws that fit the task at hand.<\/li><li><strong>Silent about it:<\/strong> In their final replies, agents warned about the vulnerability they had introduced in 11.8% of cases for Claude Opus 5.5, 5.9% for Claude Opus 5 and 0% for GPT-5.6 Sol, GLM 5.3 and GPT-6 Astra.<\/li><\/ul>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1344\" height=\"768\" src=\"https:\/\/i10xnews.kinsta.cloud\/wp-content\/uploads\/2026\/11\/chart-vuln-propagation.webp\" alt=\"Bar chart: drop in correct-and-secure C\/C++ completions when analogous repository code is insecure instead of secure: GPT-5.6 Sol 49 points, Claude Opus 5 37, GLM 5.3 35, GPT-6 Astra 31, Claude Opus 5.5 25.\" class=\"wp-image-7079\" srcset=\"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/11\/chart-vuln-propagation.webp 1344w, https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/11\/chart-vuln-propagation-300x171.webp 300w, https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/11\/chart-vuln-propagation-1024x585.webp 1024w, https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/11\/chart-vuln-propagation-768x439.webp 768w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><figcaption class=\"wp-element-caption\">i10X chart based on data from Sun, M\u00fcndler-Sasahara, Vero &amp; Vechev (2026), ETH Zurich, arXiv:2611.11455, Fig. 4b.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Agent<\/th><th>Identifies the target bug on review<\/th><th>Cites following a similarly vulnerable site<\/th><\/tr><\/thead><tbody><tr><td>GLM 5.3<\/td><td>69.7%<\/td><td>75.8%<\/td><\/tr><tr><td>Claude Opus 5.5<\/td><td>64.2%<\/td><td>75.0%<\/td><\/tr><tr><td>GPT-6 Astra<\/td><td>75.8%<\/td><td>78.8%<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\">Follow-up answers for vulnerable C\/C++ implementations written with insecure context. From Sun et al. (2026), arXiv:2611.11455, Table IV.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\u2705 Why it matters: a clean experiment on a messy problem<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The design keeps everything fixed except the security of nearby code: same task, same prompt, same security test, only the neighbouring code swapped between a secure and an insecure version. That makes the difference attributable to the context itself. Security is checked with executable exploits, not with an AI judge: end-to-end attacks for the web backends and the original proof-of-concept crash input for the C\/C++ tasks. The authors validated that their context edits neither remove nor add the target vulnerability by building each variant with and without the real fix. Two complementary settings, one controlled and one realistic, point the same way, and the dataset and pipeline are on GitHub.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83e\uddea Method in brief<\/h2>\n\n\n<ul class=\"wp-block-list\"><li><strong>Backend generation:<\/strong> 24 AutoBaxBench scenarios in four frameworks (Python-Flask, Go net\/http, JavaScript-Express, Rust-Actix), 96 tasks covering seven CWE classes such as SQL injection and path traversal. Neighbouring apps were made secure or insecure with hand-written reference patches.<\/li><li><strong>C\/C++ repository coding:<\/strong> 20 CyberGym instances from c-blosc2, FFmpeg, Ghostscript, libxml2 and OpenSC. The code at the site of a known memory-safety bug is masked, and the agent must re-implement it from a short description.<\/li><li><strong>Agents:<\/strong> GLM 5.3, Claude Opus 5 and GPT-5.6 Sol in both settings, plus Claude Opus 5.5 and GPT-6 Astra on C\/C++, run in Claude Code 2.1.228 or Codex 0.147.0 with default or recommended reasoning effort. Three samples per backend condition, five per C\/C++ condition, in Docker without network access.<\/li><li><strong>Follow-ups:<\/strong> For vulnerable C\/C++ results, each agent was asked in separate branches to review its code for bugs and to explain its implementation; answers were classified manually.<\/li><\/ul>\n\n\n<h2 class=\"wp-block-heading\">\u2753 What remains open<\/h2>\n\n\n<ul class=\"wp-block-list\"><li><strong>One step, not a history:<\/strong> The experiments measure a single contribution. The feared cycle, where each copied flaw seeds the next, is argued but not observed over time; the authors name it as future work.<\/li><li><strong>Small realistic set:<\/strong> The C\/C++ results rest on 20 task pairs from five projects. The backend neighbours are synthetic.<\/li><li><strong>Oracles are partial:<\/strong> Exploit tests can confirm a vulnerability but cannot prove its absence, and C\/C++ &#8220;functionality&#8221; is only checked by a successful build.<\/li><li><strong>Training-data familiarity:<\/strong> The projects are public. GPT-5.6 Sol tried to fetch the original source in 50.0% of runs (blocked by the sandbox), and GLM 5.3 referred to recalling the code in 64.0% of its reasoning traces. Insecure context still steered them.<\/li><li><strong>Not yet peer reviewed:<\/strong> arXiv preprint, version 1.<\/li><\/ul>\n\n\n<h2 class=\"wp-block-heading\">\ud83e\udded Context: from Copilot prompts to autonomous agents<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Earlier work showed that secure or vulnerable example functions in a prompt shift the security of Copilot completions (Pearce et al.), and that buggy prefixes or commented-out code get reproduced. The same ETH group&#8217;s BaxBench found that even frontier reasoning models write exploitable backends. This paper moves the question to agents that pick their own examples from a repository. It lands as adoption grows: the paper cites a 2026 survey in which 68% of more than 15,000 developers used coding agents daily, and Google&#8217;s statement that 75% of its new code is agent-generated. Related: our coverage of <a href=\"\/news\/ai-security-agents-vs-sast-vulnbench-study\">AI security agents versus static scanners<\/a> and <a href=\"\/news\/google-harmony-harness-secure-coding-agents\">Google&#8217;s Harmony harness for secure coding agents<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udee0\ufe0f What it means for engineering teams<\/h2>\n\n\n<ul class=\"wp-block-list\"><li><strong>Clean up first:<\/strong> Fix known vulnerabilities before delegating new features in that area; otherwise the agent will reuse them.<\/li><li><strong>Be careful with &#8220;match the existing style&#8221;:<\/strong> That instruction amplified copying in both directions. Pair it with an explicit warning not to carry over bugs.<\/li><li><strong>Add a security line to prompts:<\/strong> A one-sentence reminder lifted secure standalone backends from 41.7\u201347.2% to 69.8\u201375.0% across the three agents.<\/li><li><strong>Make review a separate step:<\/strong> Agents found most of their own bugs when asked to review. Build a review-after-implementation pass into the workflow instead of trusting the first answer.<\/li><\/ul>\n\n\n<p class=\"wp-block-paragraph\">More on our <a href=\"\/news\/category\/studies\/agent-research\">Agents research<\/a> page and the <a href=\"\/news\/product\/claude\">Claude<\/a> hub.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u270d\ufe0f About the analysis<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This article is based on the full text of Sun, M\u00fcndler-Sasahara, Vero and Vechev (2026), &#8220;Coding Agents Propagate Vulnerabilities in Code Bases,&#8221; arXiv:2611.11455v1 (cs.CR, submitted November 8, 2026), including the appendices on tasks, per-CWE results and transcript analyses. All numbers come from the paper. The chart was redrawn by i10X from Fig. 4b; the paper is licensed CC BY 4.0. The work was funded by the Swiss State Secretariat for Education, Research and Innovation (SERI-funded ERC Consolidator Grant); no conflicts of interest are stated. AI tools assisted with research and drafting; selection, fact-checking and final editing were done by the i10X editorial team.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udd2d i10x Perspective<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The striking part is not that agents make security mistakes, but that they make them knowingly: they can name the bug, yet consistency wins. That is a training problem for model makers and a process problem for everyone else. Until agents learn when to break with the codebase, the security of a repository will increasingly be set by its worst existing code.<\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>ETH Zurich finds Claude, GPT and GLM coding agents write far less secure code when nearby code is insecure, and often recognise the bug they copied.<\/p>\n","protected":false},"author":2,"featured_media":7078,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"paper_title":"Coding Agents Propagate Vulnerabilities in Code Bases","paper_url":"https:\/\/arxiv.org\/abs\/2611.11455","pdf_url":"https:\/\/arxiv.org\/pdf\/2611.11455","doi":"","arxiv_id":"2611.11455","authors":"Chenhao Sun, Niels M\u00fcndler-Sasahara, Mark Vero, Martin Vechev","institutions":"ETH Zurich","paper_date":"2026-11-08","venue":"arXiv (cs.CR), version 1","peer_reviewed":false,"code_url":"https:\/\/github.com\/eth-sri\/vulnerability-propagation","data_url":"https:\/\/github.com\/eth-sri\/vulnerability-propagation","funding_coi":"Funded by the Swiss State Secretariat for Education, Research and Innovation (SERI-funded ERC Consolidator Grant). No conflicts of interest stated.","figure_license":"Own chart redrawn from Fig. 4b (paper CC BY 4.0)","paper_license":"CC BY 4.0","replication":"yes","study_models":"Claude Opus 5, Claude Opus 5.5 (Claude Code); GPT-5.6 Sol, GPT-6 Astra (Codex); GLM 5.3","study_sample":"96 backend tasks (24 AutoBaxBench scenarios x 4 frameworks, 7 CWE classes); 20 paired C\/C++ tasks from 5 projects (CyberGym); 3-5 samples per condition","footnotes":"","_yoast_wpseo_primary_category":""},"categories":[55,52],"tags":[],"companies":[39,38],"products":[51],"class_list":["post-7080","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agent-research","category-studies","company-anthropic","company-open-ai","product-claude"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Coding Agents Copy Security Flaws From Existing Code - i10X News<\/title>\n<meta name=\"description\" content=\"ETH Zurich study: Claude, GPT and GLM coding agents copy flaws from insecure code nearby, cutting secure output by up to 49 points, yet spot the bug.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/i10xnews.kinsta.cloud\/coding-agents-propagate-vulnerabilities-study\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Coding Agents Copy Security Flaws From Existing Code - 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