-cascade-scan

cascade-scan AI Agent security evaluation framework — autom...

AgentRedBench: Dynamic Redteaming and Integration-Aware Defense for LLM Agents over SaaS Integrations

Indirect prompt injection in tool-use agents is a concrete production threat: LLM agents read from integrations third-party services such as Gmail, Salesforce, or Jira accessed through tool calls whose response content the user neither writes nor controls. Existing benchmarks under-measure the...

Needles at Scale: LLM-Assisted Target Selection for Windows Vulnerability Research

The attack surface of a modern operating system is a haystack: thousands of signed binaries and millions of functions, almost none relevant to any given vulnerability. A human analyst or an LLM agent must pick the function worth reading before analyzing it. At whole-OS scope, this target selectio...

NeuroLog: Reasoning You Can Audit -- Neuro-Symbolic Vulnerability Discovery Via LLM Facts, Datalog, and SMT

Vulnerability discovery on C/C++ source asks the analyst to choose between heavyweight static analysers, which need a working build before a single query runs, and free-form LLMs, which read source readily but invent details and lose track of cross-function dataflow on real codebases. We present...

PraisonAI's unauthenticated A2A official example can reach real LLM-driven `eval()` tool execution

Summary The first-party PraisonAI A2A server example combines three behaviors into a remotely exploitable Critical chain: 1. The example exposes an A2A server without configuring authtoken. 2. The same example binds the server to 0.0.0.0. 3. The example registers a calculateexpression tool...

Attackers Use LLM Agent for Post-Exploitation After Marimo CVE-2026-39987 Exploit

An unknown threat actor has been observed using a large language model LLM agent to conduct post-compromise actions after obtaining initial access following the exploitation of a publicly-accessible Marimo network using a recently disclosed vulnerability. "The attacker compromised an...

How to Compare the Security of Code Written by Humans to LLM-Generated Code

Large language models LLMs are rapidly transforming how software is created and maintained. Comparing LLM-generated code against human-written standards is essential to determine whether these new tools uphold or erode the security baselines established by professional developers. Yet, we lack a...

A Protocol-Language Model for Network Intrusion (Without Deep Packet Inspection)

Modern network intrusion detection systems NIDS are caught in a structural contradiction: the protocols carrying the highest threat intelligence are precisely those encrypted under TLS 1.3 and QUIC, where payload inspection yields nothing. We ask a simpler question -- what if the attack signature...

Honeyval: A Comprehensive Evaluation Framework for LLM-Powered HTTP Honeypots

Honeypots are decoy systems mimicking real system components designed to defend against cyber attacks. Recently, LLMs increasingly serve as simulation backbones for honeypots. They enable defenders to construct high-interaction honeypots with low system security risks. However, LLM-powered honeyp...

Automatically Attacking Software Reverse Engineering AI Agents

Software tools for reverse engineering executable binary files, such as Ghidra, enable malware analysts to safely conduct robust static analysis without having access to original source code. Coupled with the analytic power of large language models LLM, agentic systems enabled with tools, such as...

vLLM 安全漏洞

vLLM is an open-source LLM-based inference and service engine that features high throughput and efficient memory usage. Version vLLM 0.14.1 contains a security vulnerability caused by the hardcoding of the trustremotecode=True parameter, which may lead to remote code execution...

Towards Demystifying and Repairing LLM-In-The-Loop Vulnerabilities

Large Language ModelsLLMs have been actively integrated into modern software systems as critical components. LLM-in-the-loop vulnerabilities, where vulnerabilities are introduced by LLMs and their dependent downstream components, such as frameworks, introduce new risks. Although some benchmark...

Malicious code in @catclaw/message-logger-plugin (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector cf070f85ba454a799d80e6998ee717f0fc9084513041893a164752162e0b0864 On plugin registration, the log-collector is enabled by default and uploads session JSONL files from /.openclaw/agents//sessions to...

Exploit for Incorrect Implementation of Authentication Algorithm in Google Android

popping a calc bash am start -n com.sec.android.app.popupcalc...

VMware Spring AI 安全漏洞

VMware Spring AI is a development framework from VMware that integrates Artificial Intelligence and Large Language Modeling capabilities in the Spring ecosystem. A security vulnerability exists in VMware Spring AI versions 1.1.0 through 1.1.x. The vulnerability stems from a failure to clean up...

EUVD-2026-31598

A critical remote code execution vulnerability exists in all versions of the HuggingFace transformers library prior to version 5.3.0. The vulnerability allows an attacker to craft a malicious config.json file containing the attnimplementationinternal field set to an attacker-controlled HuggingFac...

MemMorph: Tool Hijacking in LLM Agents Via Memory Poisoning

LLM-driven agents are capable of selecting external tools to complete users' tasks. However, attackers could compromise such process, steering agents toward inappropriate/wrong tools and enabling malicious actions. Most existing attacks primarily manipulate the tool metadata, which is easily...

EUVD-2026-31345

LiteLLM prior to 1.83.10 allows a user to modify their own userrole via the /user/update endpoint. While the endpoint correctly restricts users to updating only their own account, it does not restrict which fields may be changed. A user who can reach this endpoint can set their role to proxyadmin...

EUVD-2026-31057

NVIDIA TRT-LLM for any platform contains a vulnerability in RPC testing, where an attacker could cause an unsafe deserialization. A successful exploit of this vulnerability might lead to code execution, denial of service, data tampering, and information disclosure...

CVE-2025-33255

Summary: CVE-2025-33255 affects NVIDIA TensorRT-LLM (any platform) via an MPI server deserialization vulnerability. The impact described across sources includes code execution, denial of service, data tampering, and information disclosure. The NVIDIA security bulletin specifies remediation by upd...