Tag: News

Your AI agent did exactly what it was designed to do. The framework underneath it just handed an attacker a shell on the box that holds your OpenAI key, your database credentials, and your CRM tokens.That is not a hypothetical. In a few months, three of the most widely deployed AI agent frameworks each turned a known, ordinary bug class into a way through. Check Point Research chained a SQL injection in LangGraph’s SQLite checkpointer to full remote code execution. Tenable and VulnCheck tracked a path traversal in Langflow’s file upload endpoint to active, in-the-wild RCE. Cyera documented a path traversal in LangChain-core’s prompt loader that reads your secrets off disk. Two paths to a shell, one to your keys. They are the same bug, wearing thre

Enterprise teams keep watching the same thing happen. An AI agent demos beautifully, goes to production, and stalls: it runs for a short stretch, then needs a human to top up its context and check its output, and the promised efficiency drains into supervision. The agent did the work; you did the watching. It’s one reason so many agent pilots never turn into production systems.The pitch on the other side of that wall is the one every team wants to believe: an agent that runs a long job on its own, overnight if it has to, and leaves a person to validate only the last 10%. Whether that is achievable turns on a problem the orchestration conversation mostly skips. When AI firm Chroma tested 18 leading models, every one lost accuracy as its input grew, a property of how attention works, not a gap a stronger model closes. An agent fed more and more of your business as it runs does not get steadier. It gets shakier.This is

Anthropic announced a potentially game-changing new feature for users of Claude Code on the Claude Team and Enterprise subscription plans: Artifacts. This update turns a Claude Code session's work into a live, interactive, and shareable, custom HTML webpage, allowing a Claude Code user to plug in live code, multiple data sources, and have it surface on an interactive URL that they can send to other teammates — be it a dashboard, an app design, or some other product meant for internal usage. These teammates and the original user can watch the webpage it update in real-time as Claude Code goes about its work autonomously or under the user's guidance, and as the connected data sources and codebases change. While Anthropic first introduced Artifacts to its consumer web chatbot in the summer of 2024—where it evolved from a manual toggle feature to a generally available tool for pu

Imagine your engineering team just deployed an AI agent to search through internal company documents and answer employee questions. It works perfectly in development, but in production, it consistently hallucinates or misses key constraints. Fixing this is rarely a simple patch. It requires a tedious, trial-and-error process of tweaking chunking strategies, retrieval methods, and system prompts simultaneously. Because these adjustments are entangled, it becomes nearly impossible to attribute which specific tweak actually solved the problem. To address this challenge, researchers at Renmin University of China and Microsoft Research introduced Arbor, a framework that upgrades AI-driven research and optimization from a sequence of trial-and-error guesses into a cumulative learning process. Arbor organizes hypotheses, experiments, and insights into a tree that helps the system learn from prior failures to make smarter, verified impr

Two AI tools broke in the same way in the same two weeks, and four research teams proved it. The pattern underneath every disclosure is one sentence: enterprise AI accepts external input with no trust boundary. On June 15, Varonis disclosed SearchLeak (CVE-2026-42824), a proof-of-concept exfiltration chain in Microsoft 365 Copilot Enterprise Search. A victim clicks a crafted microsoft.com URL, Copilot searches their mailbox, and the data leaves through a Bing SSRF. No plugins, no second click, no visible indicator. Four days earlier, Obsidian Security published a three-CVE chain against LiteLLM that carried a default low-privilege user all the way to admin and remote code execution. Two tools. Two teams. One broken boundary.The five-check audit at the end of this article maps each gap to a CVE or a market signal from June, a command

Adobe has announced a major expansion of its "creative agent" across its flagship Creative Cloud suite and upgraded Firefly AI studio. Available in public beta starting today across Premiere Pro, Photoshop, Illustrator, InDesign, and Frame.io, the agent is designed to serve everyone from individual creators to enterprise marketing teams. Unlike first-generation generative AI tools that simply output flat media from a chat interface, Adobe’s embedded assistant acts as an orchestration layer. It interprets natural language prompts and directly accesses the underlying software's APIs to execute complex, multi-step production workflows—from batch-renaming video sequences to dynamically updating brand assets across print layouts—while leaving the final aesthetic decisions entire

Building a context layer between enterprise data stores and AI agents is bespoke work, with no standard service to automate or maintain the graphs over time. Amazon is making a direct play to change that.Amazon on Wednesday entered the space, announcing a series of three products it's positioning as a context intelligence stack for AI agents. The centerpiece is AWS Context, a new knowledge graph service that gets smarter through agent usage over time. AWS also announced the general availability of Amazon S3 Annotations and a preview of skill assets in AWS Glue Data Catalog.The context layer is now a contested architectural category with no shortage of options from different vendors. AWS is entering that market with a different architectural premise: that the graph should learn from how agents use it automatically, without human re-curation."Your agents now get smarter without you having to rebuild anything from scratch," said Swami Sivasubramanian