AI Security | Breaking Cybersecurity News | The Hacker News

Cybersecurity researchers have disclosed a critical vulnerability in OpenAI's ChatGPT Workspace Agents that could have allowed a single phishing link to stealthily build, authorize, and deploy an autonomous artificial intelligence (AI) agent inside a victim's organization. The vulnerability has been codenamed AgentForger by Zenity Labs. The issue has since been addressed by OpenAI as of June 8, 2026, following responsible disclosure. "A single link could hijack OpenAI's ChatGPT Agent Builder to stand up an attacker-controlled AI agent with a real employee's access and its approvals switched off," the AI security company said in a two-part report shared with The Hacker News. The attack occurs when an unsuspecting employee clicks open a benign-looking ChatGPT link, causing it to spawn a new AI agent within the company's trust boundary that does the attacker's bidding. The issue is a case of cross-site request forgery (CSRF) that forges an attack...

AI agent security is moving through a familiar maturity curve: adoption, then visibility, and finally, control. But what we've collectively discovered is that enforcing least privilege for AI agents is harder than we ever imagined. This is why there are so many approaches, from prompt filtering to identity-layer access controls. Where we've collectively landed is that understanding the intent of AI agents is essential to securing them. It's not easy, but it's the only path forward. Organizations approach this challenge with different levels of sophistication. For many, the current goal is simply to find the AI agents already operating across the business. That is a necessary first step. AI agents are appearing in SaaS platforms, developer environments, cloud workflows, customer support systems, productivity tools, and internal applications. Some are sanctioned, and others are not. But discovery alone isn't useful. AI agents are not passive; they reason, plan, c...

Eight security flaws in NodeBB went public on Wednesday, along with the code to exploit them. Aikido Security rates all eight as high severity and says its AI pentest agents found them in a six-hour review of the forum software's source code. Every version before 4.14.0 is affected. NodeBB has fixed them all, and administrators should be on 4.14.2. The simplest one takes a settings change. A regular forum member could point their homepage setting at the admin address, reload the page, and the admin dashboard opened for them. No password, no exploit code. The forum's own interface blocks that setting, but the block only ran in the browser and could be sidestepped. Most of what a member could then reach was read-only, including the error log and any user list an admin had exported, though they could also swap the site logo. Two more gave an attacker with no account at all access to things meant to be private. One let anyone claim to be any user and read private messages...

Redis shipped seven security releases on July 23 after researchers published authenticated RCE PoCs for stock Redis 6.2.22, 7.4.9, 8.6.4, and 8.8.0. All four chains require RESTORE. The Streams chains also need EVAL and XGROUP; the 8.8.0 chain needs EVAL and the bundled RedisBloom module. Redis says the underlying memory flaws may lead to remote code execution . Redis 6.2.23, 7.2.15, and 7.4.10 fix the Streams shared-NACK use-after-free; Redis 8.2.8, 8.4.5, and 8.6.5 fix both the Streams issue and the RedisBloom and TDigest out-of-bounds writes; Redis 8.8.1 fixes the RedisBloom and TDigest loaders, while the Streams guard was already present in Redis 8.8.0. Two PoC targets, Redis 6.2.22 and 7.4.9, were the May security updates Redis told users to install, but those releases did not include the shared-NACK ownership guard. Upgrade to the fixed release for the deployed branch. Until then, revoke RESTORE from accounts that do not strictly need it and block untrusted network acce...

Cybersecurity researchers have uncovered a sandbox escape vulnerability in Anthropic's Claude Cowork that makes it possible to break out of the confines of a Linux virtual machine (VM) within which the agent runs to read or write files anywhere on the Mac. Accomplish AI, which shared details of the vulnerability with The Hacker News ahead of publication, said about 500,000 macOS users running local Cowork sessions were affected prior to it being patched. It has been codenamed SharedRoot . "We connected a folder to a fresh Claude Cowork session, sent one short message, and watched the agent escape the sandbox," Oren Yomtov, principal security researcher at Accomplish AI, said . "From inside the VM, it reached the host Mac and read and wrote files all over it, far outside the folder we'd connected, with no permission prompt anywhere." With this level of access, the agent can access any data stored on the Mac via the user's account, including SSH ke...

Most people understand identity theft as an attacker stealing a real person's sensitive information and impersonating them. Synthetic identity fraud is much harder to catch. Instead of stealing a real identity, the attacker manufactures a new one, frankensteining together several real data points with fabricated ones to create a person who doesn't exist. Since no real victim monitors misuse, a fake identity can silently accumulate permissions and credibility over time before it's ever detected. This same principle has a largely unexplored parallel with Non-Human Identities (NHIs). Security teams are spending significant effort to protect NHIs from being stolen. Still, the machine-side equivalent of synthetic identity fraud is rarely discussed: identities that were never legitimately provisioned from the start. Following this approach, an attacker doesn't hijack an existing service account but instead fabricates one, blending real environmental attributes with fake o...

Security leaders who build fast, visible paths to AI adoption are becoming the most valued partners in their organizations. AI governance done right gives security teams the visibility they need, employees the tools they want, and CISOs the strategic influence they have earned. According to McKinsey's State of AI report, 76 percent of employees now use AI in some capacity at work, up from 55 percent the year before. Writing assistants, coding copilots, meeting summarizers, AI-powered research tools: they are woven into daily work. Most were never reviewed by security. The conventional response is restriction. An application appears, the security team blocks it, employees find a workaround within days, and the cycle repeats. It comes down to speed: the approval path moves slower than the pace of AI releases. When the official route takes six weeks, and a workaround takes six minutes, most employees will choose the workaround. Technology gets adopted because people find it use...

OpenAI on Tuesday said a combination of its artificial intelligence (AI) models, including GPT-5.6 Sol and an "even more capable pre-release model," was behind the security incident that targeted Hugging Face's production infrastructure last week. The AI company said the models were operating with "reduced cyber refusals for evaluation purposes" that might otherwise limit their ability to conduct cyber attacks, adding it expects such incidents to "become more commonplace with the proliferation of increasingly cyber-capable models." Incidents like this may become more common. Subscribe, and we’ll alert you only when another major AI security story breaks. Describing it as an "unprecedented cyber incident" and one involving state-of-the-art cyber capabilities, OpenAI said it intends to conduct a thorough investigation in partnership with Hugging Face to get to the bottom of the matter. As part of an internal evaluation, the models...

A single invisible comment in an Azure DevOps pull request can turn a reviewer's own AI coding agent against them, driving it into projects the attacker has no rights to reach and quietly leaking what it finds. The flaw is in Microsoft's official Azure DevOps MCP server , and it works because one of its tools returns pull request descriptions without a prompt-injection guardrail the company had already applied to others. Agentic Runtime Security firm Manifold Security detailed the confused-deputy bug this week. Microsoft ships the server so AI agents can read and operate Azure DevOps for a user, across pull requests, pipelines, wikis, and work items, all with the user's own permissions. That is the whole problem: content other people wrote can become instructions the agent acts on. Azure DevOps PR descriptions accept Markdown, which allows HTML comments. In the web UI, an HTML comment (<!-- ... -->) renders as nothing, so a reviewer scrolling the description s...

Hidden text on a web page was enough to make Kiro , AWS's agentic coding IDE, rewrite its own configuration file and run an attacker's code on a developer's machine, with no approval step able to stop it. Intezer, in research with Kodem Security, found that a request as ordinary as asking Kiro to summarize a page could end in remote code execution. AWS has patched the issue and says it is covered by CVE-2026-10591. Kiro's safety model rests on a human clicking "allow." The agent can run shell commands, fetch URLs, and edit files, and the design assumes a developer reviews anything risky before it happens. That approval step is the security boundary, and the flaw let an attacker slip past it without the developer ever being offered a choice. The weak point was the file that tells Kiro which external tools to load. Kiro reads its list of Model Context Protocol servers, and the exact command used to start each one, from ~/.kiro/settings/mcp.json. When th...

An Android app that can draw over other windows and write to shared storage can slip instructions to the AI agent driving that phone, in text no human eye will ever see. Two more steps, and the same app is running commands on the PC driving the agent. Researchers demonstrated that chain, plus six other attacks, against five open-source mobile agent frameworks: AppAgent, AppAgentX, Mobile-Agent-v3, Open-AutoGLM, and MobA. Everyone fell to at least six of the seven. The paper went up on arXiv on July 1 and was revised on July 14. The authors are at Simon Fraser University, the Chinese University of Hong Kong, Shandong University, and the Xingtu Lab at Chinese security firm QAX. Nothing here has a CVE, and first author Zidong Zhang told The Hacker News the team has no evidence of the techniques being used outside a controlled setting. The Hacker News checked all five frameworks and found the screenshot paths, the shell call, and the broadcast fallback that the paper describes st...

A cloud tenant using nothing but ordinary GPU access can push a data center's power draw up and down fast enough to threaten the grid it runs on, with no exploit and no break-in. That is the claim behind Bit2Watt , described by three Zhejiang University researchers in a paper accepted to CHES 2026 , the IACR's hardware-security conference, and the evidence splits in two: they measured the power modulation on real GPUs and simulated the grid destabilization it could cause. The technique inverts the usual grid-attack model: no compromised sensors, no malware on control systems, no stolen operator credentials, just a workload built to misbehave on purpose. It works because a GPU's power draw follows whatever it is computing. Saturate the tensor cores and current spikes; drop to idle, and it collapses. Toggle between those states on a schedule and you get a controllable power oscillation at the wall socket. The authors frame it as a blunt question: can "purely com...

In an ironic twist, open-source artificial intelligence (AI) platform Hugging Face revealed that it was the victim of a hack perpetrated by an autonomous AI agent system. The company said it detected and responded to the incident targeting its production infrastructure earlier last week. "We identified unauthorized access to a limited set of internal datasets and to several credentials used by our services," the company said in a statement. While an investigation into the intrusion remains ongoing, Hugging Face said it has found no evidence that the AI agent tampered with public, user-facing models, datasets, or Spaces, and its own software supply chain. The starting point of the attack was the data processing pipeline itself, with a malicious dataset abusing two code execution paths, viz., in its remote code dataset loader and a template injection in a dataset configuration, to run code on a processing worker.

A Go botnet called NadMesh turned up in early July hunting exposed AI services, and the operator's own dashboard claims 3,811 unique AWS keys. A Shodan harvester keeps the scan queue stocked with ComfyUI, Ollama , n8n , Open WebUI , Langflow , and Gradio: the image generators, local model runners, and workflow builders that teams stand up fast and firewall late. The intel feed behind that counter shows 47 credential hauls and 41 model inventories in its last 100 records. Those inventories carry DeepSeek, GLM, and Kimi identifiers tagged :cloud, which suggests that what the bots catalogue reaches past the box itself. QiAnXin's XLab published a report on Friday, named the malware after the "n4d mesh controller" string in its source, and screenshotted the panel. The figures on it are the operator's own, captured July 10, and they do not agree with each other. A counter reading 17,700 total deploys sits above a funnel claiming 95,700 in the past 24 hours. On...

Ask an AI agent to summarize the reviews on a product page, and a single planted review can make it click "Buy Now" instead. Ask a coding assistant to apply a maintainer's fix from a GitHub thread, and a fake comment can make it run a stranger's command on your computer. Neither trick hijacks the agent's task. Each one just corrupts the facts it trusts and lets it carry on with the job you asked for. That is the shape of a new class of attack laid out in a  paper posted July 6  by researchers from Seoul National University, the University of Illinois Urbana-Champaign, and Largosoft. They call it agent data injection , or ADI. The attacker's input gets dressed up as data the agent already trusts, like a sender's name or a button's ID, so it slips past most of the defenses built to stop prompt injection. The gap comes from how an agent reads. It takes in two kinds of things: instructions, meaning what you and the app's developer tell it to d...

OpenAI has disclosed details of GPT-Red , an internal automated red-teaming model that scales prompt injection vulnerability discovery with an aim to fix issues before the tools are deployed widely. "GPT‑Red is a strong red-teamer, and our previous models are highly vulnerable to its prompt injection attacks," the artificial intelligence (AI) company said . "We use GPT‑Red to adversarially train GPT‑5.6 , making it much more robust to prompt injections." The model works just like a human red-teamer. It sends a prompt, monitors how a GPT model responds, and iterates its way towards a malicious goal, such as uploading sensitive data to an external server. The development comes as adversarial prompt injections continue to be a persistent thorn in the flesh of large language models, which can be tricked into executing a carefully crafted instruction⁠ that can produce undesirable consequences. As agentic systems continue to be hooked to third-party data sources ...

For years, routing traffic through cloud proxies was good enough. Then work moved to the browser, AI entered the workflow, and the inspection model stopped keeping up. Enterprise workflows now live across SaaS applications, browsers, and an expanding ecosystem of generative AI tools, unsanctioned browser extensions, and autonomous agents. Employees routinely paste intellectual property into public LLMs for code optimization, while automated agents query internal documentation and move data across systems at machine speed. The challenge is not that SASE failed, but that data interactions have shifted to the presentation layer, an area network-centric architectures were never designed to see. This structural paradigm shift is explored in detail within The Guide to Modern SASE Architecture . Why Traditional Enforcement Struggles Traditional SASE relies on backhauling traffic to cloud proxies for decryption, inspection, and policy enforcement. However, modern internet protocols, spe...

Open a repository in Cursor on Windows and, if a file named git.exe is sitting in the project root, Cursor runs it. No click, no approval dialog, no warning that anything in the folder is about to execute. Whatever that binary does, it does as you, with your source, your SSH keys and your cloud tokens. Cursor keeps re-running it for as long as the project stays open. No prompt injection, no agent, no model in the loop, and no prior access to the machine: opening the folder is the entire exploit, and the result is arbitrary code execution as the logged-in user. AI security firm Mindgard reported the flaw to Cursor on December 15, 2025 and  published full technical details  on Tuesday, seven months later. There is still no patch, and Cursor has published no advisory for the issue. The mechanism takes about a sentence. Cursor checks several locations for a Git binary when a project loads, and one of them is the workspace itself. Process Monitor output in t...

Any other browser extension that can run a script on claude.ai can still trigger Claude for Chrome tasks aimed at your Gmail, your latest Google Doc and its comments, and your Calendar. Both this and ClaudeBleed need a rogue extension that can already run a script on claude.ai; the difference is scope. Anthropic restricted the arbitrary-prompt path in May as part of its response to the  ClaudeBleed  flaw, boxing external callers into a fixed set of tasks, but  Manifold Security  says the gap is still open in v1.0.80, the current release, eight versions later. If you run Claude for Chrome and any other extension that can touch claude.ai, you are in scope. In the default "ask before acting" mode, the forged task still hits an approval box you have to click. If you switched on "Act without asking," the hands-off automation mode, it runs with no prompt at all. The quickest guard is to turn "Act without asking" off and review any extension with permissio...

AI security agents are starting to influence real security decisions. They summarize findings, prioritize remediation, recommend next steps, and help teams move faster. But most still rely on fragmented risk signals: scanner output, severity scores, threat intelligence, configuration findings, and exposure data. That fragmentation matters because attackers do not move through environments one tool category at a time. They chain exposures across identities, networks, cloud assets, applications, and security controls. If the AI workflow only sees isolated findings, it cannot understand whether those findings create a real attack path. As AI-powered attackers accelerate exploitation, security teams need more than faster AI-assisted workflows. They need workflows grounded in evidence that can prove which risks are exploitable. These systems can correlate information and identify patterns, but without validation, they cannot answer the question security teams ultimately care about: C...