As AI becomes more integrated into federal IT (and attacker toolsets) government agencies will need to focus their resources on regulating and monitoring the identities that access their network, a top White House cybersecurity official said Thursday.

Nick Polk, branch director for federal cybersecurity in the Executive Office of the President, said that while AI models will present unique threats to federal networks, they will still generally require trusted access first, something defenders can use to their advantage.

“I think the important thing is that in many cases in order to use and exploit the vulnerabilities that [AI] might find, or use them in a manner…that could be malicious or adversarial, the first thing you have to do is get into the network,” Polk said at the Rubrik Public Sector Summit presented by FedScoop. “There are some cases where your software is facing the internet, there’s a little bit of an easier solution there, but most times you have to get into the network.”

That often means exploiting the access an employee, contractor or third-party vendor has to your systems and data. Even in an AI-powered future, the network security boundary still matters, providing organizations with meaningful control over who gets access to their systems and data and how.

“That’s really where strong identity is still really critical in order to [first] repel an attempted exploitation before it can happen or, [second,] identify very quickly that this person or this machine really shouldn’t be on the network” or is behaving anomalously,” Polk said.

However, even before large language models emerged, cybercriminals and foreign adversaries were increasingly compromising organizations not with malware or sophisticated exploits,  but by gaining network access through stolen accounts, credentials, and other trusted assets.

Federal identity security, already a concern, is now set to become more critical in the age of AI.

Justin Ubert, director of cyber protection at the Department of Transportation, said beyond speed and scale, AI tools have given malicious hackers other advantages, like obviating the need for stealth.

“Now, you can have a smash-and-grab of your network that’s faster than you can respond to because…there’s no need to be quiet: just go in, grab and go [home],” said Ubert. “By the time your fences are working as they’re supposed to be, as we designed them to be, they’re already gone.”

AI tools can also easily become insider threats. Even when users restrict their ability to perform sensitive actions like downloading or exfiltrating data without human input, models have bypassed those guardrails by exploiting obscure technical loopholes.

Research released last month by the University of California-Riverside found that automated AI agents “can become dangerously fixated on completing assignments without recognizing when their actions are harmful, contradictory or simply irrational.”

The study, which examined Anthropic’s Claude Sonnet and Opus 4, as well as OpenAI’s ChatGPT-5, found that model agents struggled with contextual reasoning, had biases towards taking action (i.e. figuring out how to do something instead of whether to do it) and would frequently get tripped up by contradictory or infeasible goals.

Anna Libkhen, acting CISO for the Bureau of Economic Analysis at the Department of Commerce, said that AI has become “much more clever in hiding how it managed to penetrate and attack and come through as a trustworthy source.” 

When asked how the federal government was working to address current gaps in identity security that are increasingly being exploited by AI systems, Libkhen said federal leaders are “peeing in their pants” before adding “at least I am.”

“It is scary, yes, we are very vulnerable,” Libkhen said.

She compared the use of AI agents to teaching a child to ice skate: the first thing you teach them is how to handle a fall and recover. Likewise, organizations will need to plan for when their agents fail and quickly recover lost assets.

“Our agents will go wrong, they will do things we don’t expect them to. How do we get up?” said Libkhen. “Do we have that third set of data because that agent erased the database and the backup? Is it safe elsewhere? What kind of holes can you anticipate and what will it take for us to recover from those holes?”

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A new malware-based credential-stealing campaign, which researchers are calling “DeepLoad,” has been infecting enterprise business IT environments.

In a report released Monday, ReliaQuest AI researchers Thassanai McCabe and Andrew Currie say the most relevant feature of this attack is the way it uses artificial intelligence and other engineering “to defeat the controls most organizations rely on, turning one user action into persistent, credential-stealing access.”

DeepLoad is delivered to victims via “QuickFix” social-engineering techniques, such as fake browser prompts or error pages. If the user falls for the scheme, the malware developers — or more likely their AI tools — put a lot of work into building evasion of security technology “at every stage” of the attack chain.

The loader “buries functional code under thousands of meaningless variable assignments,” and the payload runs behind a Windows lock screen process that is “overlooked by security tools” monitoring for threats. ReliaQuest said “the sheer volume” of code padding likely rules out human-only involvement.

“We assess with high confidence that AI was used to build this obfuscation layer,” McCabe and Currie write. “If so, organizations should expect frequent updates to the malware and less time to adapt detection coverage between waves.”

DeepLoad can steal credentials through real-time keylogging, and even if security teams block the initial loader, it was able to persist through backup contingencies.

“In the incidents we investigated, the loader spread to connected USB drives, which means the initial host is unlikely to be the only impacted system,” McCabe and Currie wrote. “Even after cleanup, a hidden persistence mechanism not addressed by standard remediation workflows re-executed the attack three days later.”

ReliaQuest’s research offers more evidence that over the past year, some traditional static cybersecurity practices — such as searching for malware signatures or file-based patterns — may be fast becoming obsolete, as AI models can spin out endless variations of attack tooling with unique signatures.

Other organizations like Google and Anthropic have been sounding the alarm that AI-enhanced cyberattacks are dramatically shrinking the time defenders must respond to a compromise.  

At the RSA Conference in San Francisco this year, experts told CyberScoop that the next two years are set to be a “perfect storm” favoring AI-powered offense, with cybercriminals and nation-states more quickly adapting the technology to add greater speed and scale to their attacks than their defensive counterparts.

McCabe and Currie say the likely continued use of AI to frustrate static analysis monitoring means that defenders will need to shift focus to other indicators of compromise.

“Based on what we’ve observed, organizations must prioritize behavioral, runtime detection—not file-based scanning—to catch this campaign (and similar ones) early,” they wrote. 

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Professional NBA and NFL athletes were allegedly deceived and victimized by a 34-year-old Georgia man’s sneaky social-engineering scheme that he ran while impersonating a well-known adult film star, the Justice Department said Monday.

Kwamaine Jerell Ford allegedly initiated and committed some of the crimes while incarcerated in federal prison for a similar, widespread phishing scam that also targeted college and professional athletes and musical artists starting in 2015. 

“While serving time for stealing credit card numbers from athletes and celebrities to fund his lifestyle, Ford allegedly engaged in the same conduct again,” Theodore S. Hertzberg, U.S. attorney for the Northern District of Georgia, said in a statement.

The alleged repeat offender, while adopting the persona of an adult film model, tricked professional athletes into providing him their iCloud login credentials and multifactor authentication codes for those accounts to steal financial and personally identifiable information to pay for personal expenses.

Ford is accused of executing more than 2,000 unauthorized transactions on professional athletes’ debit and credit cards from November 2020 to September 2024, according to an unsealed indictment. He was in federal custody for the first 14 months of the conspiracy and released on probation for prior crimes in January 2022.

Prosecutors did not name victims, divulge how many athletes Ford allegedly victimized during his latest scheme, or how much money he obtained through the conspiracy. 

He pleaded not guilty Friday to 22 charges for crimes including wire fraud, obtaining information by computer from a protected computer, access device fraud, aggravated identity theft and sex trafficking. Ford is being held without bail pending a trial. 

Using the adult film model’s identity, Ford allegedly enticed his high-profile victims to communicate with him on social media by falsely claiming he would send them adult film content through iCloud.

When a professional athlete responded, Ford allegedly sent phishing messages to the victim designed to look like legitimate Apple customer service text messages. Officials said Ford spoofed legitimate Apple customer service accounts and posed as an Apple customer support representative to request victims’ login details via text messages.

Prosecutors said Ford told his victims the messages contained a video file shared through an iCloud link that required them to reply with an MFA code. Ford allegedly attempted to access his victims’ iCloud accounts at the same time, triggering an MFA code delivery to the victim’s device.

Professional athletes who provided their iCloud MFA codes to Ford were ultimately tricked into giving him complete access to their iCloud accounts, officials said. Ford allegedly used that access to steal sensitive data, driver’s licenses and credit card information that he used for personal spending.

Ford also, while impersonating the adult film star, allegedly victimized an OnlyFans model by claiming he would advance their career. Prosecutors said Ford enticed the OnlyFans model to engage in and record commercial sex acts with professional athletes without their consent. 

“Ford clearly did not learn from his prior conviction for a similar scheme. This time, he allegedly escalated his criminal activity — stealing identities and money while also moving into coercion and sex trafficking,” Peter Ellis, acting special agent in charge at the FBI Atlanta office, said in a statement. 

Ford allegedly advertised the victim to targeted athletes, coordinated their travel to coincide with athletes’ known locations, and negotiated payments from the athletes for sex with the victim. Prosecutors said Ford took a financial cut from those commercial sex acts, many of which the victim was coerced into filming without the athletes’ knowledge. 

Ford is also accused of using these videos from the OnlyFans model to engage with additional athletes under false pretenses. When the OnlyFans model resisted filming the sex acts, Ford allegedly coerced them to send him money in lieu of the videos.

In 2019, Ford was sentenced to three years in prison and ordered to pay restitution of almost $700,000 after he pleaded guilty to computer fraud and aggravated identity theft. That scheme, which also ran for about four years, allowed Ford to hack into more than 100 Apple accounts belonging to high-profile professional athletes and rappers. 

Ford was still in prison for those crimes when he allegedly established a new scheme targeting similar victims on some of the same technology platforms.

You can read the indictment below.

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