PPTAgent: Arbitrary Code Execution via Python eval() of LLM-Generated Code with Builtins in Scope

Summary This vulnerability has been fixed in https://github.com/icip-cas/PPTAgent/commit/418491a9a1c02d9d93194b5973bb58df35cf9d00. CodeExecutor.executeactions pptagent/apis.py:126-205 processes LLM-generated slide editing actions using Python's eval: python pptagent/apis.py:184-186 partialfunc =...

Generating Proof-Of-Vulnerability Tests to Help Enhance the Security of Complex Software

Developers create modern software applications Apps on top of third-party libraries Libs. When library vulnerabilities are reachable through application code, the applications can be vulnerable to software supply chain attacks. Prior work shows that developers often require concrete and executabl...

Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contracts

Smart contracts on blockchains are prone to diverse security vulnerabilities that can lead to significant financial losses due to their immutable nature. Existing detection approaches often lack flexibility across vulnerability types and rely heavily on manually crafted expert rules. In this pape...

CVE-2026-42079

PPTAgent (the PPTAgent framework) is affected by CVE-2026-42079 due to an arbitrary code execution flaw: Python eval() executes LLM-generated code with builtins in scope. This vulnerability existed prior to commit 418491a and has been patched in that commit. The issue is triggered locally (attack...

CVE-2026-42079 PPTAgent: Arbitrary Code Execution via Python eval() of LLM-Generated Code with Builtins in Scope

PPTAgent is an agentic framework for reflective PowerPoint generation. Prior to commit 418491a, PPTAgent is vulnerable to arbitrary code execution via Python eval of LLM-generated code with builtins in scope. This issue has been patched via commit 418491a...

FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework

Modern fuzzers increasingly use Large Language Models LLMs to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initialization and sampling variance, which can reduce exploration efficiency and lead to redundant inputs. We present FunFuzz, a multi-island evolutionary fuzzi...

Stable Agentic Control: Tool-Mediated LLM Architecture for Autonomous Cyber Defense

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QASecClaw: A Multi-Agent LLM Approach for False Positive Reduction in Static Application Security Testing

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Self-Adaptive Multi-Agent LLM-Based Security Pattern Selection for IoT Systems

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hunter-max-oss

hunter-max A bug-bounty research framework. Two pieces: 1...

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With the rapid evolution of Android applications, traditional machine learning-based detection models suffer from concept drift. Additionally, they are constrained by shallow features, lacking deep semantic understanding and interpretability of decisions. Although Large Language Models LLMs...

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PT-2026-35512

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System-Aware Contextual Digital Twin for ICS Anomaly Diagnosis

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UNSEEN: A Cross-Stack LLM Unlearning Defense against AR-LLM Social Engineering Attacks

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