trufflehog

TruffleHog Find leaked credentials. 🔎 Now Scanning ...and more To learn more about TruffleHog and its features and capabilities, visit our product page. 🌐 TruffleHog Enterprise Are you interested in continuously monitoring Git, Jira, Slack, Confluence, Microsoft Teams, Sharepoint and more for...

StealthRL

StealthRL: Reinforcement Learning Paraphrase Attacks for Multi-Detector Evasion of AI-Text Detectors Paper arXiv Demo Model Hugging Face Benchmark Dataset Hugging Face Abstract AI-text detectors are increasingly used in high-stakes settings, yet their robustness to meaning-preserving adversarial...

benign-instruction-bench

Passing the Test You Trained On Re-evaluating prompt-injection detectors where LLM agents actually use them: on the tool outputs an agent reads. Teams pick injection detectors by their benchmark scores. We check whether those scores predict behavior inside an agent, and find that they mostly...

slither

Slither, el analizador estático de contratos inteligentes Únete al Slack de Empire Hacking - Discusiones y Soporte Slither es un framework de análisis estático para Solide y Vyper escrito en Python3. Ejecuta un conjunto de detectores de vulnerabilidades, imprime información visual sobre los...

Breaking Windows Malware Detection: A Comprehensive Evaluation of Problem-Space Adversarial Robustness

Problem-space evasion attacks have exposed critical weaknesses in machine learning-based malware detectors; yet, their evaluation remains fragmented across models, datasets, and attack methodologies, often neglecting domain-specific requirements such as executability and functionality preservatio...

Input-Layer Starvation: Why Per-Layer Pruning Breaks IoT Intrusion Detectors

Intrusion detectors for small Internet-of-Things IoT devices are usually compressed by pruning and judged by overall accuracy. We show that this hides a severe class-level failure, find its cause, and give low-overhead prevention and repair. On CICIoT2023, a two-layer convolutional detector prune...

What Makes Adversarial Examples Transfer across Deepfake Detectors?

Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and transferred to a target model, unknown to the attacker. Yet how source--target compatibility shapes attack success remains poorly understood. Prior...

PromptShield Home: Ambient Multimodal Prompt Injection Defense for Smart-Home Agents

Smart-home assistants increasingly use multimodal large language models MLLMs that perceive video and audio directly. This raises a safety question specific to the home: can the agent tell a genuine user command from ambient or externally-sourced content, television speech, on-screen text, or an...

Empirical Analysis of Evasion and Poisoning against Malware Data Drift Detection

As concept drift due to malware evolution presents challenges for malware classification, machine learning-based data drift detection tools are developed to mitigate this problem. These data drift detector tools are designed for a different purpose and built with different techniques compared to...

Early Detection of Distributed Backdoors in Multi-Agent LLM Systems: A Characterization Study

Multi-agent LLM systems can be attacked by a payload that no single agent ever holds in full: a poisoned tool hides encrypted fragments in its observations, spreads them across several agents, and an external step reassembles and executes them after the run. Per-step safety checks that judge each...

ALIBI: Adaptive Agentic Attacks on LLM-Based Vulnerability Detectors Via Adversarial Code Comments

Large language models are increasingly deployed for security-sensitive tasks such as vulnerability detection and code review. Their reliance on natural-language context embedded in source code exposes a previously underexplored attack surface: adversarial comments that can influence a detector's...

EXE-Bench: Ranking the Tradeoffs of AI-Based Windows Malware Detectors for Real-World Usability

Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since existing evaluations i differ in terms of data used for both training and testing; ii do not consider temporal analysis to showcase whether models...

AIDRS-Hybrid-Intrusion-Detection

AIDRS — AI-Powered Intrusion Detection & Response System A...

CVE-2026-15143

A flaw was found in the filetype content detector of guardrails-detectors. This vulnerability allows a remote attacker to supply an arbitrary XML Schema Definition XSD string, which is processed without proper restrictions. This can lead to server-side requests to arbitrary URLs or local file...

CVE-2026-15143 Guardrails-detectors: guardrails-detectors: ssrf and local file read via user-supplied xml schema (xml-with-schema:)

A flaw was found in the filetype content detector of guardrails-detectors. This vulnerability allows a remote attacker to supply an arbitrary XML Schema Definition XSD string, which is processed without proper restrictions. This can lead to server-side requests to arbitrary URLs or local file...

CVE-2026-15143

CVE-2026-15143 affects guardrails-detectors' file_type content detector. An attacker can supply an arbitrary XML Schema (XSD) string, processed without proper restrictions, enabling server-side requests to arbitrary URLs or local file reads and risking disclosure of sensitive information.

CVE-2026-15143 Guardrails-detectors: guardrails-detectors: ssrf and local file read via user-supplied xml schema (xml-with-schema:)

A flaw was found in the filetype content detector of guardrails-detectors. This vulnerability allows a remote attacker to supply an arbitrary XML Schema Definition XSD string, which is processed without proper restrictions. This can lead to server-side requests to arbitrary URLs or local file...

CVE-2026-15143 Guardrails-detectors: guardrails-detectors: ssrf and local file read via user-supplied xml schema (xml-with-schema:)

A flaw was found in the filetype content detector of guardrails-detectors. This vulnerability allows a remote attacker to supply an arbitrary XML Schema Definition XSD string, which is processed without proper restrictions. This can lead to server-side requests to arbitrary URLs or local file...

CVE-2026-15143

A flaw was found in the filetype content detector of guardrails-detectors. This vulnerability allows a remote attacker to supply an arbitrary XML Schema Definition XSD string, which is processed without proper restrictions. This can lead to server-side requests to arbitrary URLs or local file...

CVE-2026-60086

PraisonAI before 4.6.78 contains a prompt injection defense bypass vulnerability where the injection defense only blocks threats classified as CRITICAL, requiring three or more detector families to match simultaneously. Attackers can craft single or double-vector prompt injections that are...