While AI is supposed to help defenders, it’s now creating more than twice as much noise as human-triggered incidents CrowdStrike detects as potentially malicious. The company’s threat hunting team and systems triaged an average of 14 million detection leads daily, resulting in about 36,000 customer alerts during the one-year period ending in June.

“AI agent-driven behaviors have surged past human triggers,” said Adam Meyers, senior vice president of counter adversary operations at CrowdStrike. “AI has driven the detections significantly above what humans are causing, and this gives you a sense of how frequently AI is being used, and really just that it’s being used everywhere.”

The threat posed by AI showed up incessantly during the past year, sparking alarming shifts and heightened targeting across software defects, open-source supply chains and AI tools themselves — all of which create greater difficulties for defenders, CrowdStrike said in its annual threat hunting report. 

“The AI tools that are being implemented by every enterprise across the globe right now are also creating an extended attack surface,” Meyers said during a press briefing. 

“AI is now a tool, a target, and a force multiplier for adversaries,” researchers wrote in the report, adding that AI-enabled malicious activity surged 89% during the past year as attackers used the technology to scale operations, hasten tradecraft and target AI infrastructure.

Attackers are using frontier AI models to uncover vulnerabilities and develop resources, including AI-generated scripts, payloads and commands that increase their effectiveness and efficiency. The technology also allows threat groups to design more creative ways to run automated attacks and boost impact by manipulating, interrupting or sabotaging AI systems and data.

“AI is both the weapon and the target,” Meyers said. 

Most organizations don’t view it as such, and thus far haven’t secured or put proper guardrails around the AI tools they use or address the ways attackers can use AI against them, he added. 

AI’s mark on vulnerabilities is particularly concerning, as reflected by what Meyers described as “one of the scarier stats” in this year’s report: 88% of vulnerabilities were weaponized through AI within 48 hours. 

“This is creating a rich ecosystem of vulnerabilities for attackers to use against various systems,” he said. It also renders the 30-day patch window obsolete, forcing organizations to struggle under a new baseline patch cycle of 24 to 48 hours, according to Meyers.

The AI ecosystem also became the next software supply chain battleground during the past year, as evidenced by TeamPCP’s rampage through open-source software in the first half of this year. 

The threat cluster compromised more than 300 software dependencies in one day, Meyers said. 

AI tools are already in the crosshairs and the attack surface will continue to grow as agentic systems, AI application integrations and dependency managers for AI agents proliferate, the report concluded.

“The same AI tools driving modern businesses are creating under-defended attack surfaces that adversaries are exploiting,” Meyers said. “We have to secure AI. This is absolutely critical.”

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A new Senate draft bill would establish a list of AI agent software providers that people can use to establish human ownership and securely run agents on social media and other online platforms.

The Artificial Intelligence Access, Gatekeeper Exchange, and Nondiscriminatory Transfer (AI AGENT) Act, led by Sen. Mark Warner, D-Va., would allow end users of large online platforms with more than 50 million customers or subscribers per month the right to choose at least one AI agent provider who complies with security and identity standards developed by the Federal Trade Commission.

Such agents are increasingly making decisions on behalf of users, like shopping, posting content on social media, or changing account settings, sometimes without the user’s consent or knowledge.

Under the bill, the FTC would certify independent bodies to vet AI agent vendors. These certification bodies would ensure products meet baseline protections for privacy, data security and acting in the user’s interest. The bill would also require providers to link each AI agent to its human operator’s identity and to include built-in controls that let users clearly grant or revoke permission for the agent to act on their behalf.

While the commission cannot bar platforms from using AI agent providers that fail to meet those standards, it can deregister violators from the FTC list.

The bill is a discussion draft, and Warner said he was releasing it now to receive feedback before introducing a formal version for consideration in the Senate.

“As agentic AI transforms how Americans interact with technology, consumers deserve a real choice in the marketplace – and AI agents must be accountable to the people they serve,” Warner said in a statement. “This discussion draft is a major step toward building a clear federal framework that promotes innovation, protects consumers, and ensures the United States continues to lead the world in emerging technology.”

Last year, Morgan Stanley estimated that nearly one-in-four (23%) Americans made purchases using AI over a 30-day period, and that agentic shoppers could account for potentially hundreds of billions of dollars in online commerce by 2030.

But AI agents can still be unreliable or erratic. They can make absurd purchases that a user would never knowingly approve, leak sensitive data or act contrary to a user’s interest.

As more agents flood the internet, it increases the likelihood of AI bots interacting with and buying from other AI bots – underscoring the need for safe or regulated user solutions that can verify accountable human identities behind AI activity and provide baseline security and privacy protections.

The Trump administration is trying to find its own baseline for regulating frontier models. Earlier this month the Department of Commerce placed export controls on Anthropic’s Mythos 5 and Fable 5 models, and the two parties are attempting to negotiate a framework to provide government oversight of newer releases.

An AI executive order released by the Trump administration set up a voluntary 30-day testing program for AI companies to submit certain frontier models for testing and evaluation, but the administration imposed the export controls days after Anthropic released Fable 5 publicly, reportedly citing concerns that the model could be jailbroken.

Anthropic claims that extensive internal testing has identified no universal jailbreaks for Fable 5 and that third-party research released thus far hasn’t shown that their guardrails preventing access to the model’s enhanced cybersecurity or biological capabilities have been circumvented. Those are the capabilities that Anthropic cited when it held back its newest model, Mythos, from public release.

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Government agencies, cybersecurity companies and threat researchers are pouring resources into studying how fast-developing AI tools can be wielded by malicious actors to hack into victim organizations.

But as agentic AI becomes more embedded in business infrastructure, there’s also a high possibility that a breach could be caused by an insider guiding the tool, whether maliciously or due to lack of security controls.

In research shared exclusively with CyberScoop, DTEX researchers detail how a common workflow in Anthropic’s Claude Cowork used in corporate environments offers convenience for AI agent deployment but grants near-total access to the system.

Claude Cowork includes tools that let users remotely control their agents. One particular tool, known as Dispatch, relays commands from a user’s phone to their desktop Claude agent. It also includes a plugin for communicating with Salesforce AI agents that access and transfer data.

DTEX researchers tested two scenarios. The first prompted Claude to summarize information from Salesforce and paste it into a draft Outlook email. The second tasked the agent with archiving selected files and transferring them via the Cowork app.

In both cases, researchers used simple, single-turn prompts and spent between 10-30 minutes preparing to exfil  the data.

Alex Desmond, director of insider threat intelligence and innovation at DTEX, told CyberScoop that both improvements in frontier models and deeper integration of AI tools into IT network operations have reduced the time defenders have to react to a breach.

“In cyberattacks, you talk about the kind of execution time of adversaries coming in and dropping ransomware, we’re now seeing the kill chain drop to 30 and 10 minutes depending on what they’re doing,” Desmond said. “Six months ago, that was a couple of hours.”

But that speed, when paired with direct access to business networks or cloud services, can also create an insider threat nightmare for organizations that must monitor for both malicious actors and potential mistakes from legitimate employees using the technology.

Over the past few years, western IT and cybersecurity businesses have been inundated with job applicants secretly working on behalf of the North Korean government. Their salaries are used to evade international sanctions and fund Pyongyang’s nuclear program, but it also positions the individuals to access or steal sensitive data or assets from these companies. 

“You’ve got a nation-state actor getting into an environment legitimately,” Desmond said. “Now if you gave them access to AI tools on top of that…you’re like ‘here’s the keys to everything and here’s this awesome tool that’s just going to make your job – stealing our data – easier.’”

Tests by DTEX confirmed that the agents indeed had access to sensitive systems, applications and data – including the ability to download SharePoint corporate data, production documentation in OneDrive, access to Outlook email, Salesforce data (and all the data it can access), and any other files on the user’s endpoint device. For each of these applications, Claude Cowork has a dedicated plugin or API to share externally if prompted.  

To be clear, DTEX’s research does not involve exploiting a software bug or configuration vulnerability, and it doesn’t come with a CVE. It’s more of an IT governance and visibility problem. Businesses are racing to integrate AI tools into their workflow and pushing employees to use the technology while failing to put in place the kind of security controls, access policies and monitoring required to spot problems.

For instance, it may not be possible to determine how a data breach or leakage involving an AI agent actually occurred if an organization is not logging and auditing its prompts – or whether the incident was the result of an agent running amok or responding to potentially malicious instructions.

While network and cloud monitoring can identify when data is being accessed or downloaded from SharePoint, that may not be a strong enough signal to stand out for defenders.

“If a user’s normal workflow is to pull sensitive files down to work locally all the time, you don’t have endpoint monitoring and you introduce an AI agent, it then just has access to all that data” along with the ability to exfiltrate it,” Desmond said.

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On Wednesday, Microsoft released two new red teaming tools — Rampart and Clarity — meant to help developers design more secure agentic software and assist incident responders in the face of ongoing breaches.

Rampart is built on top of PyRIT, an existing open automation framework Microsoft developed for red teaming generative AI systems. But while PyRIT scans already-built systems for security flaws, Rampart is made to continuously test code for vulnerabilities during the development process, encoding both adversarial and benign testing scenarios into the software development pipeline to flag exploitable bugs and dependencies.

Microsoft said Rampart was built to focus on cross-prompt injection attacks, where “an agent retrieves or processes potentially poisoned content from documents, emails, tickets, and other data sources that manipulate behavior indirectly.” It also confirms fixes or exploits work as intended through multiple rounds of testing, as opposed to tools that perform “single shot validation.”

The second tool, Clarity, can be run as a desktop app, a web interface or directly embedded into a coding agent to provide real time security engineering guidance to developers at the outset of a project. It can categorize and track different business objectives related to the code and highlight downstream security implications along with more secure by design alternatives.

Ram Shankar Siva Kumar, who founded Microsoft’s AI red team in 2019, told CyberScoop that the company has seen internal security benefits from using the tools, but believesRampart and Clarity’s growth depends on contributions from other developers outside the Microsoft ecosystem.

In the fast-moving world of AI, where vibe coding, rogue AI agents and a steady churn of new model releases create fresh security implications nearly every week, Siva Kumar said it was important to begin building foundational, AI-centric security processes into the software development pipeline.

“When you hear a lot of talk about AI safety and security, it seems to be a lot of philosophical debates,” he said. “You’ll see frameworks, you’ll see white papers, and I think we’re really past that time, now. We really need to start thinking of AI safety as an engineering discipline and trying to bring security where the developers are.”

Rampart’s potential utility to defenders goes beyond just securing software development pipelines. It can also be used during an active incident response to speed up or automate red teaming for hot fixes, patching and remediation.

Microsoft has used Rampart when investigating reported vulnerabilities in their own products. Siva Kumar said the tool was able to help condense a week’s worth of manual work —  replicating the vulnerability, identifying different variants of the same bug, then patching and re-testing those variants to ensure they’re no longer exploitable — into hours.

Clarity, meanwhile, acts as a security adviser for software projects, prompting developers to consider potential risks in their design decisions and their downstream security consequences. With the rise of AI-generated code and agents, and execution becoming cheaper, this kind of proactive guidance is increasingly important.

“You’re going to be able to create apps, create MCP servers to pull things out from the internet,” said Siva Kumar. “The question is, ‘should you be doing it?’ And Clarity is a step in that direction. It is asking, ‘hey, should you be doing this in the first place?’”

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