Adobe has recently addressed a critical vulnerability in its Acrobat Reader software, urging users to update immediately. The flaw, tracked as CVE-2024-41869, is a "use after free" vulnerability, which could allow attackers to execute malicious code remotely through specially crafted PDF files. This article explores the nature of this exploit, its discovery, and the urgency behind updating to the latest version.

AI guardrails are vital for ensuring the safe and responsible use of AI/large language models (LLMs). However, focusing solely on single prompt-level checks can leave organizations vulnerable to sophisticated threats. Many company policy violations and security risks can be cleverly split across multiple, seemingly innocent queries. To effectively protect against these threats, a more comprehensive approach is needed — session-level monitoring.

Over the past year, BlueVoyant’s cyber threat analysts have identified a significant rise in third-party phishing tactics, most notably with a campaign impersonating the Zelle digital payment service. By mimicking a well-known payment site like Zelle, threat actors can evade detection more effectively while collecting credentials and personally identifiable information (PII) from online users of hundreds of financial institutions.

In today's digital landscape, cyber attacks are an ever-present threat, and they all ultimately target one thing: data. For most organizations, the challenge lies not only in protecting this data but also in understanding the full scope of what they have. Many organizations struggle to identify how much sensitive data they possess, where it resides, and who has access to it.

You trust us with your most important workflows, and we take that trust seriously. In developing AI in Tines, we’ve been laser-focused on helping users leverage AI without exposing their organizations to security and privacy risks. But we also spoke with so many teams struggling to fully realize AI's potential impact. They wanted AI to do more, while still preserving those all-important security and privacy guardrails.

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Consider the case of a malicious actor attempting to inject, scrape, harvest, or exfiltrate data via an API. Such malicious activities are often characterized by the particular order in which the actor initiates requests to API endpoints. Moreover, the malicious activity is often not readily detectable using volumetric techniques alone, because the actor may intentionally execute API requests slowly, in an attempt to thwart volumetric abuse protection.