Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Initially published by the Open Worldwide Application Security Project (OWASP) in 2023, the Top 10 for LLM Application Security list seeks to bridge the gap between traditional application security and the unique threats related to large language models (LLMs). Even where the vulnerabilities listed have the same names, the Top 10 for LLM Application Security focuses on how threat actors can exploit LLMs in new ways and potential remediation strategies that developers can implement.

A criminal threat actor called “Silver Fox” is launching tax-themed phishing attacks against Japanese companies during the country’s tax season, according to researchers at ESET. “The ongoing campaign uses convincing phishing lures related to tax compliance violations, salary adjustments, job position changes, and employee stock ownership plans,” ESET says. “All emails share the same goal – trick the recipients into opening malicious links or attachments.

Public sector cybersecurity leaders are no longer measured solely on whether they stop attacks, they are measured on whether they can prove it. Across federal, state, local and education environments, compliance obligations continue to expand. Frameworks and mandates include: These are not annual checkbox exercises. They require auditable, continuous evidence of control effectiveness, and for already stretched teams, this creates a second job: compliance documentation.

As of 2026, Memcyco maintains active certifications across ISO/IEC 27001, ISO/IEC 27017, ISO/IEC 27018, and SOC 2 Type II (AICPA). These certifications confirm that Memcyco maintains independently audited processes for managing information security, securing cloud environments, and protecting sensitive data.

Anthropic just open-sourced vulnerability discovery at scale. Now what? A few weeks ago, Anthropic launched Glasswing, a $100 million initiative to use AI to identify vulnerabilities at scale. Around the same time, they introduced Claude Mythos, a system that can autonomously discover and exploit software flaws. I wrote about this trajectory in my previous analysis: AI accelerates discovery, but enterprise trust still depends on deterministic validation, remediation automation, and governance at scale.

The recent supply chain attack involving Mercor and the LiteLLM vulnerability serves as a massive wake-up call for enterprise security teams. While the security industry has spent the last year fixating on prompt injections and model jailbreaks, this breach highlights a far more systemic vulnerability. The weakest link in enterprise AI is not necessarily the model itself. It is the middleware connecting the models to your data.

‍ As GenAI tools become embedded in core business operations, the governance programs meant to oversee them are still catching up. Closing that gap requires visibility into where AI operates and the ability to express exposure in financial terms that leadership can act on. The organizations best positioned to manage AI risk are those that have already started treating it as a measurable business variable rather than an abstract operational concern. ‍

SAP environments, especially in the age of cloud work and hybrid infrastructures, are ripe with security complications. But SAP support and security is nothing to scoff at. Access controls alone in SAP environments require compliance capabilities for ultimate security, regardless of the security solution or deployment scenario.

An AI just found critical vulnerabilities that survived decades of human review. If your security program isn’t built for this moment, it’s already behind. Surprise! An AI just did what your security team couldn’t.