The post Access LLMs from the Linux CLI appeared first on AI Security Expert.

Moreover, autonomous AI penetration testers can operate continuously without the need for human intervention, providing real-time security assessments that are scalable and highly efficient. They can quickly scan vast amounts of data, evaluate attack surfaces, and exploit vulnerabilities while adapting their strategies based on the evolving security landscape. This makes them invaluable for modern DevSecOps pipelines, where security needs to be integrated at every stage of development. However, despite their benefits, there are concerns about the potential for misuse, as these bots could be co-opted by malicious actors or generate false positives if not carefully monitored and controlled. Effective management and oversight are key to harnessing the full potential of AI-driven penetration testing.

The post AI/LLM automated Penetration Testing Bots appeared first on AI Security Expert.

One of the challenges with prompt injection is that AI systems are trained on vast datasets and are designed to predict the most likely continuation of a given prompt. This makes them vulnerable to cleverly crafted injections that guide them around established content restrictions. As a result, even sophisticated filtering systems can fail to recognize these injections as malicious. Addressing this vulnerability requires continuous updates to both AI models and the filtering systems that guard them, as well as developing more context-aware filters that can detect when a prompt is subtly leading to an undesirable outcome.

The post Prompt injection to generate content which is normally censored appeared first on AI Security Expert.

One of the key challenges of transparent prompt injection is its ability to bypass conventional security mechanisms because it is often hidden in plain sight. Attackers may use invisible characters, HTML formatting, or even linguistic techniques like using homophones or synonyms to subtly embed their malicious prompt. These injections could target document-processing systems, AI-powered virtual assistants, or other applications that rely on text-based inputs, potentially exploiting the trustworthiness of a document’s content. For organizations, mitigating these attacks requires robust filtering and validation mechanisms to analyze both visible and non-visible content within documents, ensuring that malicious instructions cannot be executed through hidden manipulations.

The post Creating hidden prompts appeared first on AI Security Expert.

Moreover, data exfiltration can also occur through seemingly innocuous text formatting. Attackers may encode sensitive information in markdown elements like images or links, using these features to mask the transmission of stolen data to external servers. Since markdown is designed to enhance user experience with rich text, these hidden threats can go unnoticed, giving adversaries a stealthy way to export sensitive information. This is especially critical in environments where LLM chatbots handle personal, financial, or proprietary information. Without proper input/output sanitization and strict markdown parsing controls, chatbots become vulnerable to exfiltration attacks that can compromise data security.

The post Data Exfiltration with markdown in LLMs appeared first on AI Security Expert.

In prompt injection scenarios, this conversion is particularly useful because many input validation systems expect inputs in a specific character set, like ASCII, and might not be configured to handle Unicode properly. For example, an attacker could use Unicode homographs or encode certain special characters like semicolons or quotation marks that are typically filtered in ASCII form but pass through unnoticed when represented in Unicode. Once bypassed, these encoded characters can still be interpreted by the target system in their original form, allowing the attacker to execute malicious commands or manipulate outputs. This method of encoding to bypass input restrictions can become a key vulnerability in poorly secured prompt handling systems.

The post Prompt Injection with ASCII to Unicode Tags appeared first on AI Security Expert.

The post LLM Expert Prompting Framework – Fabric appeared first on AI Security Expert.

In addition to its library, Hugging Face offers a model hub that hosts thousands of pre-trained models, accessible via simple APIs. These models can be used directly or fine-tuned for specific applications, making it easier to experiment and deploy machine learning models without extensive computational resources. The company’s tools have become indispensable in both academia and industry, with a strong emphasis on open science and ethical AI development. Hugging Face also integrates well with other popular machine learning frameworks, such as TensorFlow and PyTorch, making it a go-to resource for AI practitioners working across different platforms.

The post LLMs, datasets and playgrounds (Huggingface) appeared first on AI Security Expert.

The post Free LLMs on replicate.com appeared first on AI Security Expert.

The post GitHub repos with prompt injection samples appeared first on AI Security Expert.