Hardcoded secrets in source code pose critical security vulnerabilities which can be easily exploited by malicious adversaries. Existing regex-based detection approaches suffer from fundamental limitations, as secrets often lack identifiable patterns, resulting in poor precision and recall. Recent studies have explored context-aware detection methods, as surrounding code can reveal the purpose of candidate strings. However, these methods confront three key challenges: (1) obfuscation robustness where models over-rely on easily obfuscated identifiers, (2) cross-language generalization difficulties due to uneven training data distribution, and (3) lengthy and noisy context that introduces excessive irrelevant tokens and slows inference. We observe that strings serve as a critical information source for code semantics, offering superior contextual density, obfuscation robustness, and language independence. Based on this insight, we propose StringGroup, a novel context extraction algorithm that mines strings surrounding potential secrets. By introducing a relatively simple modification to existing patterns that narrows the analysis specifically to string literals, the method achieves significant gains. With only 33.2% of the original context, it preserves over 80% of semantic information and significantly improves the signal-to-noise ratio for secret detection. We further design a context-aware secret detection tool, Secretron, based on StringGroup methods and Transformer model. Evaluation on the SecretBench dataset demonstrates high accuracy with 98.74% F1-score and strong robustness under obfuscation and cross-language scenarios, outperforming state-of-the-art LLM-based baselines. We deploy our tool in real-world environments and successfully detect 48 previously unknown secret keys from 26 applications, demonstrating the practical effectiveness of our approach.
Zhengdong Huang, Kevin Li, Jinqiu Yang et al.· 0 citations
AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with autonomous access to local files and tools. Coding agents inherit security risks from both the LLM backbone, where adversarial prompts, poisoned training data, and backdoor triggers can cause models to emit insecure or attacker-chosen code, and their agentic architecture, where tool-using autonomy enables induced misuse of external APIs, data exfiltration, and persistent compromise of development environments. This paper presents a systematic evaluation of malicious issue requests against state-of-the-art coding agents (Cursor, Claude Code, and Codex Desktop), powered by two major model families (OpenAI GPT-5.3 Codex/GPT-5.4 and Anthropic Sonnet 4.6). Our novel benchmark IssueTrojanBench contains malicious issues that are constructed based on four novel attack categories (i.e., embedded as malicious instructions in issues), six delivery vectors (e.g., PDF, or issue comment), and further augmented by perturbations. Our results reveal critical vulnerabilities in the as-deployed modern coding agents, i.e., 66.5% of the malicious issues from IssueTrojanBench penetrate all the guardrails (agent- and LLM-level) of coding agents. Our further analysis shows that rejection is almost entirely from LLMs rather than the agent frameworks, with GPT models broadly vulnerable and Sonnet 4.6 exhibiting more selective, risk-aware blocking of high-impact actions. Our evaluation also highlights that the current agent-level defense strategy offers limited additional protection for coding agents. Our findings highlight the urgent need for stronger agent- and model-level safety mechanisms to protect AI coding agents.
ECLoop is presented, an execution layer that interposes between the agent and the repository to enforce evidence-conditioned execution and shows that each of ECLoop's three operations contributes distinct value and that structured evidence conditions outperform an equivalent natural-language summary.
Yisen Xu, Chenglin Li, Zehao Wang et al.· 1 citation