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.

Email remains the primary communication channel in healthcare, carrying patient records, referral notes, billing data, and administrative correspondence that frequently contains electronic Protected Health Information (ePHI). For Managed Service Providers (MSPs) serving healthcare clients, HIPAA-compliant email archiving is no longer optional. It is a foundational requirement for supporting regulatory compliance, responding to audits, and protecting against data loss.

A missing null check in libssh’s SFTP directory listing code lets a malicious server crash clients, but real-world exploitability is extremely constrained. CVE-2026-0968 is an out-of-bounds heap read in sftp_parse_longname(), triggered when an SFTP client processes a crafted SSH_FXP_NAME response with a malformed longname field. Red Hat, which serves as the CNA (CVE Numbering Authority) for this vulnerability, scored it 3.1 (Low), while Amazon Linux independently scored it 4.2 (Medium).

Security teams deploying AI agents into Kubernetes know they need behavioral baselines. The concept is straightforward: define what “normal” looks like for each agent, then detect when behavior drifts in ways that suggest compromise. The problem is that AI agents are designed to change. A model update alters inference latency. A prompt revision shifts tool-calling sequences. A new MCP integration adds API destinations nobody flagged during the last security review.

A platform security engineer gets an alert at 2:14 a.m. One of the LangChain agents running in their production Kubernetes cluster has produced an execution graph with eleven nodes, seven tool calls, and an egress edge to a domain that is not in the agent’s approved integration list. The chain is fully rendered in their console. Every signal is there.

Your engineering lead is in your office Thursday morning. They want to push an AI agent to production next Tuesday. It’s a LangChain-based workflow agent, connected through MCP to three internal tools and one external API, with access to a customer database. The framework posters are on the wall. Your team has spent two quarters standing up runtime observability. And sitting in that chair, you still don’t know whether to say yes.

Shadow IT refers to any technology—including hardware, software, cloud services, SaaS applications, or AI tools—used within an organization without the explicit approval of the IT or security department. Shadow IT is rarely malicious. It is usually the result of employees searching for a means of making their workflows more efficient. When sanctioned corporate tools are perceived as too slow, rigid, or complex, users often "self-serve" by adopting unvetted alternatives to meet their deadlines.

Surprise, surprise, agentic AI is advancing very quickly, and security isn’t quite keeping up. While most attention in recent times has focused on improving model capability, we’ve often been left wondering how to actually make these systems safe enough to trust with real-world tasks and limited interaction. This challenge has become particularly evident with the rise of platforms like OpenClaw, where autonomous agents can execute multi-step actions with minimal human oversight.

Every so often, something comes along that forces you to recalibrate how you think about cyber risk. Not incrementally, but fundamentally. Claude Mythos feels like one of those moments. The cybersecurity industry has spent decades racing attackers to close vulnerabilities faster. Claude Mythos suggests that race may be entering an entirely new phase. One where speed itself becomes the defining risk factor.

Anthropic’s Mythos announcement is not just another cybersecurity headline. It is a signal. AI is transforming software faster than security teams can adapt. The organizations that win won’t be the ones that simply find more flaws. They’ll be the ones that can prove their software can be trusted. A signal that software risk has entered a new era; one where AI can accelerate both the creation of software and the discovery of its weaknesses faster than human teams can respond.