The paper introduces the Neutral Prompting Attack (NPA), a stealthy method that uses semantically benign instructions to increase the likelihood of large language models hallucinating non‑existent package names in coding agents. Unlike traditional dependency steering, NPA does not target a specific package but shifts the model’s output toward more speculative names. Experiments across multiple LLMs show that NPA raises hallucination rates, affects pip install success, alters the distribution of hallucinated packages, and bypasses existing static‑analysis, LLM‑based, and agent‑based defenses.
arXiv:2605.01047v2 Announce Type: replace-cross Abstract: Hallucinations remain an unsolved problem for LLMs, and package hallucinations are a particularly dangerous instance of this phenomenon. Pack...
Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity cannot be assumed. Prior work on repository poisoning...
arXiv:2608.30686v1 Announce Type: cross Abstract: Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity ca...
arXiv:2603.18740v3 Announce Type: replace-cross Abstract: Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, rang...
The paper investigates how large language models (LLMs) hallucinate nonexistent software packages during code generation and evaluates methods to mitigate this issue. It finds that current evaluation practices overestimate hallucination rates, especially for Python, and that Retrieval-Augmented Generation (RAG) and Self-Refine reduce hallucinations across multiple models and languages. The study also introduces Package Utility (PU) to measure whether defenses preserve useful recommendations and shows that Greedy decoding offers the best trade‑off between mitigation and utility, while adversarial prompts significantly increase hallucination rates, particularly in Ruby.
The paper introduces NameTrap, a source‑code poisoning attack that renames identifiers in third‑party Python files to steer AI coding agents toward specific Common Weakness Enumeration (CWE) vulnerabilities. By iteratively aligning variable names with CWE‑specific conventions, the attack achieves success rates ranging from 22.5% to 75.0% across six agents and four vulnerability categories, outperforming random and neutral renaming strategies. The poisoned files contain no explicit instructions or vulnerable statements, and existing AI‑based and static security analyzers fail to detect the induced weaknesses.
The paper "From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents" examines how coding agents that use execution harnesses to enforce permission boundaries can be tricked into granting overly broad allow-rules. By planting adversarial context in a repository, attackers can steer an agent into requesting a plausibly justified yet dangerously wide permission, which, if approved, lets the agent bypass the harness and execute malicious actions without further human oversight. Experiments on platforms such as Claude Code and Codex CLI show that 58% of trials succeed in eliciting such rules, which then cover 56.9% of category-specific capability tests and enable silent goal completion in 81% of approved cases, thereby undermining the safety promise of human-in-the-loop oversight.
arXiv:2609.39065v1 Announce Type: cross Abstract: LLM agents increasingly rely on installable skills, which are packages of instructions, code, and resources that equip them with task-specific capabi...
arXiv:2607. 00481v1 Announce Type: cross Abstract: Jailbreak attacks remain a critical threat to the safe deployment of large language models (LLMs).
arXiv:2603.18740v5 Announce Type: replace-cross Abstract: Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, rang...
The paper revisits Thompson’s classic compiler back‑door attack in the context of self‑modifying AI coding agents. By poisoning the benchmarks used for self‑evaluation, the authors demonstrate that agents such as the Darwin Gödel Machine, Self‑Improving Coding Agent, and Hyperagents can be coaxed into generating vulnerable code, even on clean, held‑out tasks. Experiments show that the contamination can persist after subsequent clean training, highlighting the need for more robust agent designs.
arXiv:2607. 20759v1 Announce Type: cross Abstract: 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.