Malicious Package

Overview prompt-engineering-toolkit 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...

Malicious code in prompt-engineering-toolkit (npm)

Ten packages published by npm user asdxzxc at version 1.0.10 target developers working on AI and LLM tooling. Each package masquerades as a developer utility while executing a two-stage payload triggered via postinstall: package.json → lib/setup.js → lib/worker.js. Credential harvesting:...

MAL-2026-4282 Malicious code in prompt-engineering-toolkit (npm)

Ten packages published by npm user asdxzxc at version 1.0.10 target developers working on AI and LLM tooling. Each package masquerades as a developer utility while executing a two-stage payload triggered via postinstall: package.json → lib/setup.js → lib/worker.js. Credential harvesting:...

The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code

Malware authors have traditionally relied on polymorphic techniques to produce variants in the same malware family, complicating signature-based detection. Integrating generative AI into offensive toolchains enables attackers to synthesize structurally diverse payloads with identical behavior,...

Leveraging Large Language Models for Trustworthiness Assessment of Web Applications

The widespread adoption of web applications has made their security a critical concern and has increased the need for systematic ways to assess whether they can be considered trustworthy. However, "trust" assessment remains an open problem as existing techniques primarily focus on detecting known...

From Rookie to Expert: Manipulating LLMs for Automated Vulnerability Exploitation in Enterprise Software

LLMs democratize software engineering by enabling non-programmers to create applications, but this same accessibility fundamentally undermines security assumptions that have guided software engineering for decades. We show in this work how publicly available LLMs can be socially engineered to...

COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers

This paper studies how multimodal large language models MLLMs undermine the security guarantees of visual CAPTCHA. We identify the attack surface where an adversary can cheaply automate CAPTCHA solving using off-the-shelf models. We evaluate 7 leading commercial and open-source MLLMs across 18...

EUVD-2025-199055

Malicious code in prompt-eng npm...

Beyond Fixed and Dynamic Prompts: Embedded Jailbreak Templates for Advancing LLM Security

As the use of large language models LLMs continues to expand, ensuring their safety and robustness has become a critical challenge. In particular, jailbreak attacks that bypass built-in safety mechanisms are increasingly recognized as a tangible threat across industries, driving the need for...

Prompt Engineering Vs. Fine-Tuning for LLM-Based Vulnerability Detection in Solana and Algorand Smart Contracts

Smart contracts have emerged as key components within decentralized environments, enabling the automation of transactions through self-executing programs. While these innovations offer significant advantages, they also present potential drawbacks if the smart contract code is not carefully design...

How Can We Effectively Use LLMs for Phishing Detection?: Evaluating the Effectiveness of Large Language Model-Based Phishing Detection Models

Large language models LLMs have emerged as a promising phishing detection mechanism, addressing the limitations of traditional deep learning-based detectors, including poor generalization to previously unseen websites and a lack of interpretability. However, LLMs' effectiveness for phishing...

Semantic-Aware Fuzzing: an Empirical Framework for LLM-Guided, Reasoning-Driven Input Mutation

Security vulnerabilities in Internet-of-Things devices, mobile platforms, and autonomous systems remain critical. Traditional mutation-based fuzzers -- while effectively explore code paths -- primarily perform byte- or bit-level edits without semantic reasoning. Coverage-guided tools such as AFL+...

From Legacy to Standard: LLM-Assisted Transformation of Cybersecurity Playbooks into CACAO Format

Existing cybersecurity playbooks are often written in heterogeneous, non-machine-readable formats, which limits their automation and interoperability across Security Orchestration, Automation, and Response platforms. This paper explores the suitability of Large Language Models, combined with Prom...

Talking like a Phisher: LLM-Based Attacks on Voice Phishing Classifiers

Voice phishing vishing remains a persistent threat in cybersecurity, exploiting human trust through persuasive speech. While machine learning ML-based classifiers have shown promise in detecting malicious call transcripts, they remain vulnerable to adversarial manipulations that preserve semantic...

LLMalMorph: on the Feasibility of Generating Variant Malware Using Large-Language-Models

Large Language Models LLMs have transformed software development and automated code generation. Motivated by these advancements, this paper explores the feasibility of LLMs in modifying malware source code to generate variants. We introduce LLMalMorph, a semi-automated framework that leverages...

Securing Generative AI Agentic Workflows: Risks, Mitigation, and a Proposed Firewall Architecture

Generative Artificial Intelligence GenAI presents significant advancements but also introduces novel security challenges, particularly within agentic workflows where AI agents operate autonomously. These risks escalate in multi-agent systems due to increased interaction complexity. This paper...

LASHED: LLMs and Static Hardware Analysis for Early Detection of RTL Bugs

While static analysis is useful in detecting early-stage hardware security bugs, its efficacy is limited because it requires information to form checks and is often unable to explain the security impact of a detected vulnerability. Large Language Models can be useful in filling these gaps by...

SecRepoBench: Benchmarking LLMs for Secure Code Generation in Real-World Repositories

This paper introduces SecRepoBench, a benchmark to evaluate LLMs on secure code generation in real-world repositories. SecRepoBench has 318 code generation tasks in 27 C/C++ repositories, covering 15 CWEs. We evaluate 19 state-of-the-art LLMs using our benchmark and find that the models struggle...

Prompt Engineering Techniques with Spring AI

This blog post demonstrates practical implementations of Prompt Engineering techniques using Spring AI. The examples and patterns in this article are based on the comprehensive Prompt Engineering Guide that covers the theory, principles, and patterns of effective prompt engineering. The blog show...

Researchers Reveal 'Deceptive Delight' Method to Jailbreak AI Models

Cybersecurity researchers have shed light on a new adversarial technique that could be used to jailbreak large language models LLMs during the course of an interactive conversation by sneaking in an undesirable instruction between benign ones. The approach has been codenamed Deceptive Delight by...