FBI Agent Accused Of Stealing Nearly $1 Million In Cryptocurrency ⁠13%

By Sean Lyngaas⁠10%

8/4/2026, 8:40:00 AM

BS Summary: This article contains 17 faulty reasoning types, including Negativity Bias, Framing Effect, and Biased Writer Voice, with Unattributed Quote as the most egregious example at 23.7% saturation with 111 hits. Analysis detected 706 faulty-reasoning hits from 469 analyzed words, generating a BS Score of 23.3% and a BS Rank of ⁠13% (24,003 of 27,323 articles). This article is better (less manipulative) than 87.80% of the article peer group.

By Sean Lyngaas, CNN 
(CNN) — An FBI agent stole more than $900,000 in cryptocurrency from accounts the agency was monitoring after he said he became frustrated by what he saw as government inaction over the misuse of crypto accounts, according to court records made public on Monday. 
The agent, Patrick Steven Yaroch, came across the crypto holdings while a member of the FBI’s national security investigative squad focused on an “adversarial nation” and grew “frustrated when he could not do more to disrupt” an unnamed person’s use of the crypto accounts, according to an affidavit in Yaroch’s case. 
Using his internal agency access, he later allegedly transferred the crypto holdings to his personal account. 
The FBI fired Yaroch, and he was arrested last week. 
A criminal complaint lists charges against him related to the interstate transport of stolen goods and the receipt of stolen goods. 
An attorney for Yaroch was not listed in court records. 
CNN has contacted an email associated with Yaroch for comment. 
The FBI gathers a slew of intelligence on cryptocurrency accounts allegedly used by criminal and state actors in Russia, China and elsewhere. 
But when and how to act on those accounts – whether to seize them or use them to collect more intelligence – is often decided on a case-by-case basis. 
The affidavit also reflects how artificial intelligence and cryptocurrencies are changing criminal investigations. 
The month before his arrest, Yaroch allegedly asked ChatGPT: “If you had a bucket of money (around $1 million) and you wanted to leave the USA and become a resident or citizen of an EU country, what would you do?” 
ChatGPT responded, according to the affidavit, with an answer catered to Yaroch’s age and lifestyle. 
“At 37, with a young family, a goal of potentially retiring around 40, and a clear interest in eventually building a slower-living vineyard/agricultural lifestyle in places like Cilento or Portugal’s Dão region…” the AI agent reportedly said. 
“As soon as the FBI became aware of these allegations, we immediately took action, began an investigation, and ultimately executed an arrest warrant for this individual last week,” an FBI spokesperson told CNN. 
“We hold our employees to the highest ethical standards, and this conduct is not tolerated at the FBI. 
We are conducting a thorough investigation in the aftermath, and as this is an ongoing matter, we will have no further comment.” 
The affidavit includes emotional details the FBI says were gathered from interviews with Yaroch and a former colleague from the FBI’s Boston Division in which Yaroch confided. 
“I f—d up,” Yaroch told FBI agents when they interviewed him at his house last month, according to the affidavit. 
The-CNN-Wire 
™ & © 2026 Cable News Network, Inc., a Warner Bros. 
Discovery Company. 
All rights reserved. 
Article reasoning-pattern comparisonThis article: 0.0%Sean Lyngaas: 0.0%CNN: 0.9%Confirmation Bias0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.2%Anchoring Bias0.0%This article: 10.0%Sean Lyngaas: 2.5%CNN: 1.3%Availability Heuristic10.0%This article: 4.7%Sean Lyngaas: 1.2%CNN: 0.5%Representativeness Heuristic4.7%This article: 2.1%Sean Lyngaas: 0.5%CNN: 0.1%Hindsight Bias2.1%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.4%Overconfidence Bias0.0%This article: 12.8%Sean Lyngaas: 3.2%CNN: 3.5%Framing Effect12.8%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.1%Loss Aversion0.0%This article: 3.8%Sean Lyngaas: 1.0%CNN: 0.5%Status Quo Bias3.8%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Sunk Cost Effect0.0%This article: 4.7%Sean Lyngaas: 1.2%CNN: 2.1%Optimism Bias4.7%This article: 0.0%Sean Lyngaas: 0.0%CNN: 1.4%Pessimism Bias0.0%This article: 21.5%Sean Lyngaas: 5.4%CNN: 3.7%Negativity Bias21.5%This article: 7.0%Sean Lyngaas: 1.8%CNN: 2.0%Self-Serving Bias7.0%This article: 0.0%Sean Lyngaas: 2.7%CNN: 1.6%Fundamental Attribution Error0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Actor-Observer Bias0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 1.7%In-Group Bias0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.3%Out-Group Homogeneity Bias0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 2.0%Halo Effect0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Horn Effect0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Dunning-Kruger Effect0.0%This article: 8.5%Sean Lyngaas: 2.1%CNN: 0.8%Recency Bias8.5%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Primacy Effect0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Blind-Spot Bias0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.9%Ad Hominem0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.4%Straw Man0.0%This article: 10.9%Sean Lyngaas: 2.7%CNN: 2.3%Appeal to Authority10.9%This article: 0.0%Sean Lyngaas: 0.0%CNN: 1.0%False Dilemma0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.3%Slippery Slope0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.2%Circular Reasoning0.0%This article: 0.0%Sean Lyngaas: 0.7%CNN: 2.0%Hasty Generalization0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Red Herring0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.2%Bandwagon0.0%This article: 2.1%Sean Lyngaas: 0.5%CNN: 2.9%Appeal to Emotion2.1%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.3%Begging the Question0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.6%Post Hoc (False Cause)0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.2%Tu Quoque0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.1%Burden of Proof0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Appeal to Nature0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Composition/Division0.0%This article: 4.3%Sean Lyngaas: 1.1%CNN: 0.9%Anecdotal4.3%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.1%No True Scotsman0.0%This article: 7.9%Sean Lyngaas: 2.0%CNN: 1.0%Ambiguity (Equivocation)7.9%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.1%Middle Ground0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.1%Personal Incredulity0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Special Pleading0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Genetic Fallacy0.0%This article: 23.7%Sean Lyngaas: 5.9%CNN: 1.3%Unattributed Quote23.7%This article: 7.9%Sean Lyngaas: 2.0%CNN: 0.5%Quote-first Misdirection7.9%This article: 12.8%Sean Lyngaas: 3.2%CNN: 1.2%Biased Writer Voice12.8%This article: 5.8%Sean Lyngaas: 1.4%CNN: 0.4%Indoctrination5.8%This article: 0.0%Sean Lyngaas: 0.0%CNN: 1.0%Politically Left Leaning Bias0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Politically Right Leaning Bias0.0%This article: 0.0%Sean Lyngaas: 0.0%CNN: 0.0%Attempt to Sell a Product or S…0.0%

469 words analyzed.

Speakers

2speakers20%attributed speech376writer words
Selected voice

Yaroch

100%flagged-word coverage
20 attributed words22% of attributed speech74% writer coverage
0%50.0%100.0%Unattributed Quote+75.8 ptsWriter: 24.2%Yaroch: 100.0%100.0%Biased Writer Voice-16.0 ptsWriter: 16.0%Yaroch: 0.0%0.0%Quote-first Misdirection-9.8 ptsWriter: 9.8%Yaroch: 0.0%0.0%Indoctrination-7.2 ptsWriter: 7.2%Yaroch: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.