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我一直没有装龙虾，因为我最开始就用Claude code，所有的skills、subagent、mcp都是给Claude code，搞到龙虾里觉得麻烦。 后来龙虾出来后，接聊天软件那个功能，确实还不错，于是找平替，找到cc-connect，用了半个月，着实提升了我不少工作效率 开发者在群里解决问题和响应需求非常快，尊重用户，因此想帮着宣传下 详细功能大家可以去readme看 简单讲几个推荐理由 1.易上手：一边接了几乎全CLI式的AI coding工具，另一边接了几乎全聊天工具，也就是说你以前用哪个做生产力cc/codex/opencode，这玩意儿都能用起来 2.更新快！：微信刚发的ClawBot插件，今天下午cc-connect就集成了 3.用户需求真响应：我两周前刚用上的时候，发现还不能切目录，只能一个项目配一个机器人，有点傻，今天想着更新下接入微信，发现上周cc-connect就把多工作区、切换目录功能做上去了 现在用起来很丝滑，基本解决了电脑不在手边的焦虑 项目地址：https://github.com/chenhg5/cc-connect/

霍尔木兹海峡的封锁导致世界再次面临严重的石油能源危机。全球约五分之一石油和液化天然气的运输是经过霍尔木兹海峡，此次危机受影响最大的是亚洲地区。与之前的石油危机不同的是，在很多国家可再生能源已能与化石燃料展开竞争。两大人口大国中国和印度都扩大了可再生能源规模，中国仍然依赖燃煤发电，其可再生能源的规模远超印度。国际能源署的数据显示，中国约十分之一的汽车是电动汽车。中国仍然是世界最大的原油进口国，也是伊朗石油的最大买家。但通过可再生能源实现部分经济领域的电气化，中国已降低了对进口石油的依赖。如果没有这种转变，中国受到影响会更显著。印度目前正面临烹饪用燃气短缺问题，燃气短缺促使居民去抢购电磁炉。太阳能和风能只占日本能源产出的 11%，与印度持平，低于中国的 18%。巴基斯坦加速发展太阳能使该国自 2020 年以来减少进口化石燃料逾 120 亿美元。孟加拉国能源储备有限，该国已关闭大学以节省用电，政府开始实行燃料配给制。

Currently trending CVE - Hype Score: 1 - Windows Themes Spoofing Vulnerability

Currently trending CVE - Hype Score: 18 - A pre-authentication denial of service vulnerability exists in React Server Components versions 19.0.0, 19.0.1 19.1.0, 19.1.1, 19.1.2, 19.2.0 and 19.2.1, including the following packages: react-server-dom-parcel, react-server-dom-turbopack, and react-server-dom-webpack. The ...

Anvilogic has launched Blueprints, a workflow automation capability that captures expert analyst practices and turns them into scalable, repeatable workflows across security teams. Instead of requiring specialized engineers to build and maintain code, Blueprints lets analysts author automation in natural language, deploy it the same day, and have it execute to automate processes across data onboarding, detection engineering, threat hunting, investigation and response. “Your best analyst, at infinite scale,” said Mackenzie Kyle, Chief Product Officer … More →

The post Anvilogic’s Blueprints replaces SOAR complexity with natural language security automation appeared first on Help Net Security.

A survey of 500 U.S. CISOs published by Vorlon ahead of RSAC 2026 found that 99.4% of organizations experienced at least one SaaS or AI ecosystem security incident in 2025. Only three out of 500 reported zero incidents. The numbers get more uncomfortable from there. One in three enterprises dealt with a security incident involving..

The post Vorlon Survey: 99% of Organizations Got Hit by a SaaS or AI Security Incident in 2025 appeared first on Security Boulevard.

Another week, another reminder that the internet is still a mess. Systems people thought were secure are being broken in simple ways, showing many still ignore basic advisories. This edition covers a mix of issues: supply chain attacks hitting CI/CD setups, long-abused IoT devices being shut down, and exploits moving quickly from disclosure to real attacks. There are also new malware tricks

Astrix Security has revealed a major expansion of its AI agent security platform, covering every layer where AI agents operate in the enterprise: from managed AI platforms to shadow deployments running on managed devices, detecting both agent existence and unauthorized access to enterprise resources, and enforcing policy over what agents are allowed to do. AI governance programs are not built for the speed at which agents are being deployed. Like third-party risk committees that never … More →

The post Astrix advances AI agent security platform to govern shadow and enterprise agents appeared first on Help Net Security.

Ridge Security released PurpleRidge 3.0 at RSAC 2026, a self-service penetration testing platform that uses agentic AI to give small and mid-sized businesses the kind of offensive security validation that has traditionally required dedicated teams and six-figure budgets. The upgrade marks a shift from the platform’s earlier machine-learning architecture to one built on agentic AI,..

The post Ridge Security Brings Agentic AI Pentesting to SMBs With PurpleRidge 3.0 appeared first on Security Boulevard.

Straiker has launched Discover AI and expanded Defend AI to secure coding agents, productivity agents, and custom-built agent platforms. Agents are operating across enterprise systems with broad access, growing autonomy, and zero security oversight. That’s why Straiker built Discover AI and Defend AI: to give security teams visibility into what agents are running and protection against what they might do. Coding agents like Cursor, Claude Code, and GitHub Copilot are transforming how enterprise software gets … More →

The post Straiker enables visibility and runtime protection for enterprise AI agents appeared first on Help Net Security.

RSA opened RSAC 2026 with a new deployment model for its ID Plus identity platform, aimed squarely at government agencies, financial services firms, and critical infrastructure operators that need identity security to work even when everything else fails. RSA ID Plus Sovereign Deployment is a “deploy anywhere” identity and access management solution that gives organizations..

The post RSA Launches ID Plus Sovereign Deployment for Organizations That Can’t Afford Identity Downtime appeared first on Security Boulevard.

A vulnerability was found in BRI KlinikaXP Insertino and KlinikaXP. It has been classified as critical. Impacted is an unknown function. The manipulation leads to hard-coded credentials. This vulnerability is documented as CVE-2026-1958. The attack can be initiated remotely. There is not any exploit available. Upgrading the affected component is recommended.

A vulnerability was found in Nexxt Solutions Nebula 300+ up to 12.01.01.37 and classified as problematic. This issue affects some unknown processing of the component Authentication Interface. Executing a manipulation can lead to improper restriction of excessive authentication attempts. This vulnerability is registered as CVE-2026-31851. It is possible to launch the attack remotely. No exploit is available.

Cloudflare’s Gen 13 servers double our compute throughput by rethinking the balance between cache and cores. Moving to high-core-count AMD EPYC ™ Turin CPUs, we traded large L3 cache for raw compute density. By running our new Rust-based FL2 stack, we completely mitigated the latency penalty to unlock twice the performance.

Cloudflare's Gen 13 servers introduce AMD EPYC™ Turin 9965 processors and a transition to 100 GbE networking to meet growing traffic demands. In this technical deep dive, we explain the engineering rationale behind each major component selection.

AI isn’t just moving fast. It’s creating new attack paths. Cyber teams must now manage vulnerabilities – and their ramifications throughout their IT environments – in AI tools deployed without enough governance guardrails. The answer for securing this new attack surface? Unified exposure management.

Key takeaways

1. AI as an attack vector: By connecting to core workflows and sensitive cloud data, AI models have transformed from isolated targets into active attack vehicles.
2. The AI governance gap: Rapid deployment has outpaced security controls, resulting in high-risk ecosystems plagued by over-privileged access, inactive “ghost” identities, and vulnerable software supply chains.
3. Unified exposure management is essential: Securing AI requires abandoning siloed security dashboards in favor of unified exposure management that maps the complex web of interactions, identities, and permissions surrounding the models.

Most organizations have focused their AI efforts on speed, prioritizing fast adoption and experimentation to boost productivity. However, a different reality is emerging for cybersecurity teams. 

While AI creates rapid business value, it also introduces a new class of cyber exposure that extends far beyond large language models (LLMs) and expands the existing attack surface. The result? It’s now much harder to secure the organization’s entire IT environment. 

Vulnerabilities in popular AI models

Cybersecurity teams are now fully aware that the AI tools their employees are using contain serious vulnerabilities.

Tenable Research recently uncovered seven vulnerabilities and attack techniques in OpenAI’s ChatGPT, including indirect prompt injection, persistence, evasion, bypassing of safety mechanisms, and the exfiltration of personal user information. These flaws could have allowed attackers to stealthily extract private information from users’ memories and chat history.

In a separate study, Tenable Research discovered three vulnerabilities in Google Gemini that exposed users to serious privacy risks across Cloud Assist, Search Personalization, and the Browsing Tool. Again, the significance of the discovery went beyond the specific vulnerabilities. It showed that AI itself can become the attack vector and not merely the target. 

When AI systems connect to search functions, cloud logs, saved preferences, tools, and browsing services, the model can become part of the attack path to sensitive data. The risk is no longer confined to protecting the model from misuse. You must understand how threat actors can manipulate the model to reach into its surrounding environment.

Moreover, the risk does not begin and end with AI-native tools alone. Vulnerabilities in connected data platforms can also create downstream AI exposure, including the potential for data poisoning. Take Tenable Research’s recent discovery of nine novel flaws in Google Looker Studio, a popular business intelligence tool. Those vulnerabilities, which we collectively named “LeakyLooker,” could have allowed attackers to expose, alter, and delete sensitive cloud data.

AI tools are getting incorporated into orgs’ critical operations

According to the recent “Tenable Cloud and AI Security Risk Report 2026,” 70% of organizations now depend on AI and model context protocol packages as core components of their production cloud stack. 

What’s more, Deloitte’s “State of AI in the Enterprise” report, published in January, suggests that this shift is happening fast. Employee access to AI rose by 50% in 2025, and the number of companies with 40% or more in-production AI projects is expected to double in the next six months, according to the Deloitte report.

In other words, the environments in which these risks could play out are no longer on the edge of the business as pilot programs or proof-of-concept trials. They are being embedded into live environments, connected to the core workflows, and increasingly relied upon to support daily operations. 

Unfortunately, governance has not kept pace with deployment. In many organizations, AI is being operationalized faster than security teams can fully map, assess, or control the risk it introduces. It has gone from an experimental side project to mimicking the actions of a full-time employee before organizations have even set their security permissions. This gap is particularly critical in the case of agentic AI tools, which are designed to act autonomously. 

The critical importance of AI governance

Governing AI usage requires organizations to clearly define which AI services employees are allowed to use and to prevent sensitive data from being sent to external models. The following example, detected by Tenable AI Exposure, part of the Tenable One Exposure Management Platform, shows a real case of an employee attempting to access sensitive company information, followed by an attempt to exfiltrate that data through an external AI service.
 

This is more than a visibility problem. In some environments, it is already a problem of overprivileged identities. The “Tenable Cloud and AI Security Risk Report 2026” found that 18% of organizations have granted AI services administrative permissions that are rarely audited, creating a ready-made set of elevated privileges that attackers could exploit.

And that risk does not sit neatly within the model itself. Much of the danger lies in the surrounding infrastructure, such as the identities and permissions that support AI services behind the scenes.

More than half (52%) of non-human identities hold excessive permissions, while 37% are inactive “ghost” identities, according to the Tenable report. In AWS environments specifically, 73% of Amazon SageMaker roles and 70% of Bedrock agent roles were found to be inactive. On paper, those roles may look harmless or forgotten. In practice, they represent dormant access paths that can expand the blast radius of an incident. 

This is what makes AI risk so difficult to contain. The exposure is often buried in the machine identities, inherited privileges, and back-end connections that most organizations are not closely watching. 

The same pattern appears in the software supply chain. AI systems do not operate in isolation. They are built on layers of third-party code, open-source packages, external integrations, and access to providers. That creates another set of dependencies that can quietly widen risk. 

In the Tenable report, we found that 86% of organizations have at least one third-party code package with a critical vulnerability, while 13% have deployed third-party packages with a known history of compromise. 

Add to that the finding that 53% of organizations have granted third parties access to internal systems via external accounts with highly risky, excessive permissions, and the AI risk picture becomes clearer. 

These findings show that AI exposure isn’t limited to what a model might do. It extends to the broader ecosystem, such as the code, access, and trust relationships that attackers may be able to exploit long before anyone notices.

Manage AI risk with exposure management

Because AI security risks are often created through the interactions of AI tools with surrounding infrastructure and identities, managing these risks requires more than just siloed monitoring of AI tools. It demands a new approach, not another disconnected dashboard telling security teams that AI exists in their environment. That new approach is exposure management.

Exposure management gives CISOs the big picture. It enables their teams to proactively identify, prioritize, and close their organization's highest-risk cyber exposures — the vulnerabilities, misconfigurations, and identity weaknesses that combine to create attack paths leading to their organization's most sensitive systems and data. It gives them visibility and context across their entire attack surface — IT, OT, cloud, identities, and AI — so they can see AI security risks alongside IT, OT, cloud, and identity security risks and the ways in which they combine.

With exposure management, you’re able to not just detect the AI systems your employees are using, but also more deeply how they’re using them, and where AI workloads, agents and tools are running. Critically, exposure management also surfaces how these systems, via the interconnectedness with other assets, expand your cyber risk.

Here’s what we’ve found regarding the prevalence of AI in real-world scenarios. Among Tenable customers that had at least one security finding over the past year, 54% had AI tools in their environments. The most commonly detected tool was Google Gemini, followed by ChatGPT and Anthropic’s Claude. At least 1% of these customers had ClawdBot present in their environments, which could introduce disproportionate risk due to its access to sensitive data, and ability to take actions across systems.

But, as stated before, visibility alone is not enough, organizations need context. To effectively reduce risk, they need to understand what the workload can access, which permissions it requires, what external dependencies it introduces, and how attackers might chain those elements together into a real path to breach.

In short, the conversation should no longer focus on securing AI as a standalone innovation project. Security teams should zero in on securing the full web of relationships around it. 

In this AI era, exposure does not start and end with an AI model. It sits in the identities attached to it, the code feeding it, the cloud services extending it, and the data it can quietly reach. As organizations adopt AI at full speed, cybersecurity teams must focus on detecting the exposures that come with it before attackers do.

Download the "Tenable Cloud and AI Security Risk Report 2026."

A vulnerability has been found in Nexxt Solutions Nebula 300+ up to 12.01.01.37 and classified as problematic. This vulnerability affects unknown code of the component Configuration Backup Handler. Performing a manipulation results in unprotected storage of credentials. This vulnerability is cataloged as CVE-2026-31850. The attack must originate from the local network. There is no exploit available.

Max станет единственным легальным способом связи для власти.

A vulnerability, which was classified as problematic, was found in dropbox/arekinath/handnot2 esaml. This affects an unknown part of the component SAML Handler. Such manipulation leads to xml external entity reference. This vulnerability is listed as CVE-2026-28809. The attack may be performed from remote. There is no available exploit. You should upgrade the affected component.