exfil-scan

exfil-scan Scan LLM outputs and AI-generated content for data exfiltration signals before they reach users, logs, or downstream systems. 1 Why exfil-scan exists Modern LLM applications routinely process sensitive prompts, internal documents, API responses, and user data. That creates a new securi...

CVE-2026-85694 LaVague 0.2.35 Remote Code Execution via eval extraction

LaVague 0.2.35 contains a remote code execution vulnerability in PythonFromMarkdownExtractor.extractasobject that evaluates untrusted language model output derived from web page content. Attackers can inject malicious Python code through web pages using indirect prompt injection to execute...

EUVD-2026-71143

LaVague 0.2.35 contains a remote code execution vulnerability in PythonFromMarkdownExtractor.extractasobject that evaluates untrusted language model output derived from web page content. Attackers can inject malicious Python code through web pages using indirect prompt injection to execute...

CVE-2026-85694 LaVague 0.2.35 Remote Code Execution via eval extraction

LaVague 0.2.35 contains a remote code execution vulnerability in PythonFromMarkdownExtractor.extractasobject that evaluates untrusted language model output derived from web page content. Attackers can inject malicious Python code through web pages using indirect prompt injection to execute...

CVE-2026-85694

LaVague 0.2.35 is vulnerable to remote code execution via its PythonFromMarkdownExtractor.extract_as_object function, which uses eval on untrusted language model output derived from web page content. An attacker can perform indirect prompt injection through a web page to inject malicious Python c...

Topological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log Analysis

Large Language Models LLMs are increasingly being deployed in cybersecurity operations to assist cybersecurity analysts with rapid decision-making against emerging threats. However, there is a main criteria that must be met when using LLMs in cybersecurity, that is, trust in the generated outputs...

CVE-2026-72742

DSPy 3.3.0b1 contains a file exfiltration vulnerability in the Image and Audio output field adapters that allows attackers with influence over language model outputs to read arbitrary local files by injecting a filesystem path into the url field of a parsed Image or Audio typed output. The...

CVE-2025-65368: Improper Neutralization of Input During Web Page Generation

SparkyFitness v0.15.8.2 is vulnerable to Cross Site Scripting XSS via user input and LLM output...

CVE-2024-57783

The desktop application in Dot through 0.9.3 allows XSS and resultant command execution because user input and LLM output are appended to the DOM with innerHTML in render.js, and because the Electron window can access Node.js APIs...

GHSA-2HMP-5WQG-F24H PlotAI eval vulnerability

A vulnerability, that could result in Remote Code Execution RCE, has been found in PlotAI. Lack of validation of LLM-generated output allows attacker to execute arbitrary Python code. PlotAI commented out vulnerable line, further usage of the software requires uncommenting it and thus accepting t...

CVE-2024-21552

All versions of SuperAGI are vulnerable to Arbitrary Code Execution due to unsafe use of the ‘eval’ function. An attacker could induce the LLM output to exploit this vulnerability and gain arbitrary code execution on the SuperAGI application server...

CVE-2024-21552

All versions of SuperAGI are vulnerable to Arbitrary Code Execution due to unsafe use of the ‘eval’ function. An attacker could induce the LLM output to exploit this vulnerability and gain arbitrary code execution on the SuperAGI application server...

CVE-2024-21552

All versions of SuperAGI are vulnerable to Arbitrary Code Execution due to unsafe use of the ‘eval’ function. An attacker could induce the LLM output to exploit this vulnerability and gain arbitrary code execution on the SuperAGI application server...

CVE-2024-21552: Improper Control of Generation of Code

All versions of SuperAGI are vulnerable to Arbitrary Code Execution due to unsafe use of the ‘eval’ function. An attacker could induce the LLM output to exploit this vulnerability and gain arbitrary code execution on the SuperAGI application server...