SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code Via Prompt Optimization

LLM coding agents now generate code at an unprecedented scale, yet LLM-generated code introduces cybersecurity vulnerabilities into codebases without human involvement. Even when frontier models are explicitly asked to write secure production code with relevant weaknesses to avoid in context, we...

Heimdallr: Characterizing and Detecting LLM-Induced Security Risks in GitHub CI Workflows

GitHub Continuous Integration CI workflows increasingly integrate Large Language Models LLMs to automate review, triage, content generation, and repository maintenance. This creates a new attack surface: externally controllable workflow inputs can shape LLM prompts and outputs, which may in turn...

Autonomous Adversary: Red-Teaming in the Age of LLM

Language Model Agents LMAs are emerging as a powerful primitive for augmenting red-team operations. They can support attack planning, adversary emulation, and the orchestration of multi-step activity such as lateral movement, a core enabling capability of advanced persistent threat APT campaigns...

Profiling for Pennies: Unveiling the Privacy Iceberg of LLM Agents

Large Language Models LLMs have revolutionized how information are collected, aggregated, and reasoned. However, this enables a novel and accessible vector of privacy intrusion: the automated and in-depth personal profiling; this engenders a chilling effect of "peepers everywhere". Existing...

AgentTrust: Runtime Safety Evaluation and Interception for AI Agent Tool Use

Modern AI agents execute real-world side effects through tool calls such as file operations, shell commands, HTTP requests, and database queries. A single unsafe action, including accidental deletion, credential exposure, or data exfiltration, can cause irreversible harm. Existing defenses are...

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 =...

GHSA-89G2-XW5C-V95P 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

Agentic systems involved in high-stake decision-making under adversarial pressure need formal guarantees not offered by existing approaches. Motivated by the operational needs of security operations centers SOCs that must configure endpoint detection and response EDR policies under adversarial...

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

Static Application Security Testing tools help developers find security vulnerabilities before release, but they often produce many false positives. This increases manual review effort, reduces developer trust, and may cause real vulnerabilities to be ignored among noisy reports. We present...

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

The adoption of Internet of Things IoT systems at the network edge of smart architectures is increasing rapidly, intensifying the need for security mechanisms that are both adaptive and resource-efficient. In such environments, runtime defence mechanisms are no longer limited to detection alone b...

hunter-max-oss

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

CVE-2026-42208

A flaw was found in LiteLLM. A database query used for proxy API key checks incorrectly incorporated caller-supplied key values directly into the query. This vulnerability allows an unauthenticated attacker to send a specially crafted Authorization header to any Large Language Model LLM API route...

MARD: A Multi-Agent Framework for Robust Android Malware Detection

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...

Large Language Models As Explainable Cyberattack Detectors for Energy Industrial Control Systems

In modern energy systems, industrial control systems ICS and power-system SCADA require intrusion detection that is not only accurate but also auditable by operators. The ICS intrusion-detection landscape is currently dominated by established supervised detectors. In this paper, we study whether ...

CVE-2026-7141 vllm KV Block kv_cache_interface.py has_mamba_layers uninitialized resource

A vulnerability was found in vllm up to 0.19.0. The affected element is the function hasmambalayers of the file vllm/v1/kvcacheinterface.py of the component KV Block Handler. Performing a manipulation results in uninitialized resource. It is possible to initiate the attack remotely. The attack is...