Fact-checking Mullin’s claim that foreign adversaries can access voting machines 20%

By Loreben Tuquero31%

7/22/2026, 10:25:36 PM

BS Summary: This article contains 26 faulty reasoning types, including Availability Heuristic, Hasty Generalization, and Framing Effect, with Appeal to Authority as the most egregious example at 29.6% saturation with 206 hits. Analysis detected 1,067 faulty-reasoning hits from 696 analyzed words, generating a BS Score of 34.4% and a BS Rank of 20% (16,539 of 20,441 articles). This article is better (less manipulative) than 80.90% of the article peer group.

Department of Homeland Security Secretary Markwayne Mullin recently said, in unequivocal terms, that foreign adversaries can access voting machines and flip votes, echoing President Donald Trump’s narrative about interference in the 2020 election. 
“We know for sure that our foreign adversaries, not our allies, foreign adversaries have parts that are vital pieces in our voting machines,” Mullin said July 17, adding that they can access “what they consider the key to the back” of the voting machines. 
“We know that they can change voter registration and your vote. 
We know it’s possible. 
There’s not a question. 
It’s not even for debate.” 
We asked the Department of Homeland Security for more details but it did not give any; it reiterated its call for states to help guard against noncitizen voters. 
The White House referred us to that department. 
(Noncitizen voting in federal elections is rare .) 
The process of registering to vote does not involve voting machines. 
But Mullin’s comments about voting machines reflect concerns that security and election technology experts have long had. 
Voting machines have weaknesses that can allow foreign adversaries to tamper with voting machine hardware and weaken the machines’ security, said J. 
Alex Halderman, University of Michigan computer science and engineering professor who studies electronic voting machines. 
Hardware attacks would allow foreign actors to manipulate the machines’ security controls. 
But so far, there’s no proof of such attempts. 
“It’s important to note that there’s no evidence that voting machine vulnerabilities have ever been used to attack a real U.S. election, and of course the existence of vulnerabilities does not mean those vulnerabilities will imminently be exploited,” Halderman told PolitiFact via email. 
The U.S. 
Senate Select Committee on Intelligence noted these risks in a 2019 report on Russian interference in the 2016 election. 
The report said that in an experiment at a security conference, participants were able to hack into voting machines to change votes, install software and perform other unauthorized functions. 
But this was possible in part because the hackers were given physical access to the machines. 
Under real circumstances, election officials implement various measures to secure voting equipment. 
Machine access is restricted, and they are stored in locked areas with video surveillance. 
Election officials keep track of where machines are moved and who accessed them. 
Voting machines are typically not connected to the internet or to each other, said a 2024 National Intelligence Council assessment of foreign threats after the Nov. 5, 2024, election. 
Halderman was part of an academic group in 2006 that analyzed the security of certain voting machines that let voters cast and store their votes electronically , sometimes without a paper record of their vote. 
“What we found was disturbing,” Halderman told the Senate intelligence committee in 2017. 
“We could reprogram the machine to invisibly cause any candidate to win.” 
In 2021, as part of a Georgia court case , Halderman also simulated a vote-stealing tactic in which an attacker hides hardware in a ballot printer to swap which candidate was marked on the ballot. 
The judge gave him complete access to machines, and experts argued that Halderman’s findings were not realistic. 
“If you leave the keys in an unlocked car and you’re surprised I can drive away in it, that’s not a measure of the car’s security,” Tammy Patrick, a former Maricopa County, Arizona, elections administrator, told Votebeat in 2022. 
In a 2021 report , federal intelligence agencies said they did not find proof of foreign actors attempting to manipulate election processes in the 2020 U.S. election. 
Attempts to do so at scale would likely be detected through physical and cybersecurity monitoring or in audits, the report read. 
Andrew Appel, Princeton University professor emeritus of computer science who studies the technology policy of voting machines, said most voting machines today are more secure than the ones designed in 2000. 
The vulnerabilities that still exist are why most states still use paper ballots that are marked by hand and recountable by hand, Appel said. 
Paper ballots provide a way to verify and audit votes. 
RELATED: Vivek Ramaswamy has called for ‘paper ballots.’ 
Most Americans vote that way already. 
Article reasoning-pattern comparisonThis article: 0.7%Loreben Tuquero: 2.0%@politifact: 2.0%Confirmation Bias0.7%This article: 0.0%Loreben Tuquero: 0.7%@politifact: 0.6%Anchoring Bias0.0%This article: 12.2%Loreben Tuquero: 5.7%@politifact: 2.6%Availability Heuristic12.2%This article: 1.1%Loreben Tuquero: 1.0%@politifact: 1.0%Representativeness Heuristic1.1%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.5%Hindsight Bias0.0%This article: 7.5%Loreben Tuquero: 5.8%@politifact: 1.3%Overconfidence Bias7.5%This article: 10.3%Loreben Tuquero: 6.1%@politifact: 3.6%Framing Effect10.3%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.3%Loss Aversion0.0%This article: 4.5%Loreben Tuquero: 0.7%@politifact: 0.2%Status Quo Bias4.5%This article: 3.9%Loreben Tuquero: 0.6%@politifact: 0.0%Sunk Cost Effect3.9%This article: 5.0%Loreben Tuquero: 4.3%@politifact: 0.8%Optimism Bias5.0%This article: 0.0%Loreben Tuquero: 0.5%@politifact: 1.0%Pessimism Bias0.0%This article: 7.3%Loreben Tuquero: 2.0%@politifact: 5.1%Negativity Bias7.3%This article: 0.0%Loreben Tuquero: 0.4%@politifact: 0.6%Self-Serving Bias0.0%This article: 0.0%Loreben Tuquero: 0.1%@politifact: 0.3%Fundamental Attribution Error0.0%This article: 2.4%Loreben Tuquero: 0.4%@politifact: 0.1%Actor-Observer Bias2.4%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.6%In-Group Bias0.0%This article: 0.0%Loreben Tuquero: 1.0%@politifact: 0.4%Out-Group Homogeneity Bias0.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.8%Halo Effect0.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.0%Horn Effect0.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.0%Dunning-Kruger Effect0.0%This article: 4.2%Loreben Tuquero: 0.7%@politifact: 1.0%Recency Bias4.2%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.2%Primacy Effect0.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.0%Blind-Spot Bias0.0%This article: 0.0%Loreben Tuquero: 0.2%@politifact: 0.4%Ad Hominem0.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.2%Straw Man0.0%This article: 29.6%Loreben Tuquero: 9.2%@politifact: 4.6%Appeal to Authority29.6%This article: 0.0%Loreben Tuquero: 1.4%@politifact: 0.8%False Dilemma0.0%This article: 0.0%Loreben Tuquero: 0.8%@politifact: 0.5%Slippery Slope0.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.1%Circular Reasoning0.0%This article: 11.1%Loreben Tuquero: 4.8%@politifact: 3.0%Hasty Generalization11.1%This article: 1.1%Loreben Tuquero: 0.2%@politifact: 0.2%Red Herring1.1%This article: 0.9%Loreben Tuquero: 0.1%@politifact: 0.6%Bandwagon0.9%This article: 1.9%Loreben Tuquero: 2.5%@politifact: 2.5%Appeal to Emotion1.9%This article: 1.3%Loreben Tuquero: 1.4%@politifact: 0.6%Begging the Question1.3%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 1.7%Post Hoc (False Cause)0.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.1%Tu Quoque0.0%This article: 7.9%Loreben Tuquero: 3.1%@politifact: 0.6%Burden of Proof7.9%This article: 0.9%Loreben Tuquero: 0.1%@politifact: 0.2%Appeal to Nature0.9%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.2%Composition/Division0.0%This article: 5.0%Loreben Tuquero: 1.7%@politifact: 1.0%Anecdotal5.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.1%No True Scotsman0.0%This article: 7.9%Loreben Tuquero: 3.2%@politifact: 2.1%Ambiguity (Equivocation)7.9%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.2%Middle Ground0.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.1%Personal Incredulity0.0%This article: 5.6%Loreben Tuquero: 0.9%@politifact: 0.0%Special Pleading5.6%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.1%Genetic Fallacy0.0%This article: 10.3%Loreben Tuquero: 9.5%@politifact: 1.7%Unattributed Quote10.3%This article: 4.7%Loreben Tuquero: 2.0%@politifact: 1.8%Quote-first Misdirection4.7%This article: 4.7%Loreben Tuquero: 1.6%@politifact: 1.6%Biased Writer Voice4.7%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 1.0%Indoctrination0.0%This article: 0.0%Loreben Tuquero: 0.7%@politifact: 0.2%Politically Left Leaning Bias0.0%This article: 0.0%Loreben Tuquero: 0.0%@politifact: 0.4%Politically Right Leaning Bias0.0%This article: 1.1%Loreben Tuquero: 0.2%@politifact: 0.4%Attempt to Sell a Product or S…1.1%

696 words analyzed.

Speakers

4speakers40%attributed speech420writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 10 words • 0.0% coverageWriter's voice • 33 words • 100.0% coverageMarkwayne Mullin • 44 words • 100.0% coverageMarkwayne Mullin • 11 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 28 words • 100.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageJ. Alex Halderman • 22 words • 0.0% coverageJ. Alex Halderman • 15 words • 0.0% coverageJ. Alex Halderman • 12 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageJ. Alex Halderman • 43 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageJ. Alex Halderman • 13 words • 0.0% coverageJ. Alex Halderman • 12 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageTammy Patrick • 39 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageAndrew Appel • 31 words • 0.0% coverageAndrew Appel • 24 words • 0.0% coverageAndrew Appel • 10 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 6 words • 0.0% coverage
Selected voice

Markwayne Mullin

100%flagged-word coverage
55 attributed words20% of attributed speech75% writer coverage
0%40.0%80.0%Unattributed Quote+73.3 ptsWriter: 6.7%Markwayne Mullin: 80.0%80.0%Quote-first Misdirection-7.9 ptsWriter: 7.9%Markwayne Mullin: 0.0%0.0%Biased Writer Voice-7.9 ptsWriter: 7.9%Markwayne Mullin: 0.0%0.0%Attempt to Sell a Product -1.9 ptsWriter: 1.9%Markwayne Mullin: 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.