Concealing LLM-Based Multi-Agent Topology Via Phantom Structure Injection

Driven by the rapid advancement of large language models LLMs, LLM-based multi-agent systems MAS have emerged as a powerful paradigm for collaborative reasoning over complex tasks. A key design element of MAS is the communication topology, which governs information flow among agents and often...

PT-2026-102899

crmne/ruby llm at commit fa6f279847d6d7027814539d9c0dfc3bbdfd2a83 contains polynomial-time regular expression denial-of-service conditions in think-tag response parsing on Ruby 3.1.x. A malicious or anomalous model response containing many unterminated tags can cause excessive CPU consumption in...

VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents

Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems LLM-ARS instantiate users and items as autonomous agents, whose semantic states are dynamically refined through a recurrent process known as collaborative reflection. While this mechanism improves...

Practical Secrets Extraction against Black-Box LLMs

Large language models LLMs increasingly power autonomous coding agents such as Codex and Claude Code, yet their training corpora may contain confidential credentials exposed in public repositories or collected from private development artifacts, creating risks of memorization and subsequent...

Agent-Warden: EBPF-Based Kernel-Native Process-File Provenance Tracking for LLM Agents

LLM agents execute dynamically generated process and file operations that are often invisible to application-layer tracing. We present Agent-Warden, an extended Berkeley Packet Filter eBPF-based provenance monitor for tracking task and regular-file states across process creation, file access, and...

HARDE: Optimizing Agent Harnesses for Runtime Risk Detection and Execution Control

Large language model LLM agents are vulnerable to safety risks such as injected malicious instructions or misleading information, motivating runtime defenses that prevent unsafe action in execution across diverse risks while preserving benign-task utility. Existing system-level defenses either...

ToolFence: Fine-Grained Authorization for Secure Tool-Using LLM Agents

Tool-using LLM agents remain vulnerable to indirect prompt injection because trusted instructions and untrusted observations share one context, allowing malicious content to steer consequential input-filtering defenses. Multi-path consensus defenses still leave a high attack success rate because...

Confidence-Guided Protocol IR for LLM-Aided Security Protocol Modeling

Large language models offer a promising interface for translating natural-language protocol descriptions into formal security models, but their outputs remain difficult to trust without expert validation. In this paper, we present a human-in-the-loop framework for generating Tamarin-verifiable...

Pikit: A Composable Toolkit for Indirect Prompt Injection Research and Evaluation

Indirect prompt injection embeds malicious instructions within external content retrieved by LLM-based agents, altering target behavior without user authorization. We introduce pikit, a research toolkit designed to systematically evaluate these threats across three core dimensions: attacks 13...

Backdoor Mitigation in Decentralized LLM Fine-Tuning

Decentralized large language model LLM fine-tuning lets organizations collaboratively train a shared LLM on data they cannot pool, without a central coordinator. In every round, each node exchanges a trainable adapter with its neighbors over a communication graph, and then aggregates them. This...

Malicious code in llm-nebula (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 0eee293a9973027154eea71635c14e8a4cb2ad8a1e4f80a65399ad874afcb669 Package presents itself as an LLM SDK nebula.js exposes a small client stub but declares preinstall: node preinstall.cjs in package.json, and...

MAL-2026-17230 Malicious code in llm-nebula (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 0eee293a9973027154eea71635c14e8a4cb2ad8a1e4f80a65399ad874afcb669 Package presents itself as an LLM SDK nebula.js exposes a small client stub but declares preinstall: node preinstall.cjs in package.json, and...

How we found 24 Android vulnerabilities using our open source AI security agent

With the rise of AI in the security space, our team created the GitHub Security Lab Taskflow Agent as a way for security researchers to easily automate, package, and share the AI prompts and workflows that they find effective for their work. In this blog post, I'll share how I created auditing...

Malicious code in nebula-llm (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector b33da6aef41209f654f8f76cc56074a7b827599f42f941e3917326da1f4d2566 The npm package [email protected] ships a preinstall lifecycle script preinstall.cjs that embeds a 257KB Windows PE binary as a base64+zlib-compressed...

MAL-2026-17227 Malicious code in nebula-llm (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector b33da6aef41209f654f8f76cc56074a7b827599f42f941e3917326da1f4d2566 The npm package [email protected] ships a preinstall lifecycle script preinstall.cjs that embeds a 257KB Windows PE binary as a base64+zlib-compressed...

Malicious Package

Overview nebula-llm is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this package authorship...

Malicious Package

Overview llm-nebula is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this package authorship...

Similarity Is Not Validity: Defending LLM Semantic Caches against Poisoning

Semantic caches reduce LLM serving costs by reusing previously generated answers for semantically similar queries. However, retrieval is based solely on embedding similarity between the incoming query and cached queries. This design enables cache poisoning: an attacker can cache a malicious...

LLM-Assisted Automatic Security Proofs for Cryptographic Protocols: How Far Are We?

Whitepaper called LLM-Assisted Automatic Security Proofs For Cryptographic Protocols: How Far Are We?...

Tokenized Key-Gated Adapter Routing: A Secure Access Control Mechanism against Private Data Leakage in LLMs

Large language models LLMs are increasingly deployed in privacy-critical domains e.g., healthcare, finance, and government, but their propensity to memorize and disclose personally identifiable information PII poses serious security and compliance risks. Existing defenses typically force a...