The Ransomware Trust Paradox: How and Why Cybercriminals Want You To Trust Them

Ransomware is a cybercrime idea as old as the computer itself. The idea is simple: break into a victim’s computer, encrypt all their files, and demand payment for decryption. That being said, the social dynamics at play during this interaction are anything but simple. Generally speaking, the cybercriminal has one goal: to get paid. In order for the victim to pay, they must trust that upon payment they will get their files back. This simple dichotomy gives rise to the Ransomware Trust Paradox

The Paradox

The Ransomware Trust Paradox, an idea coined by Max Smeets in April 2025, refers to the idea that inherently untrustworthy individuals or groups engaging in illegal access to often sensitive data must convince their victims of their trustworthiness to meet their ends (Smeets, 2025). This shows a marked difference between ransomware and other forms of both digital and conventional crime. Think about a bank robbery: both the robber and the ransomware operator have the same goal, to make money, but the bank robber is never trusted by the victim. Rather, they achieve their ends by force. What makes this idea so fascinating to me is the lengths ransomware groups will go to to secure trust from victims. To get an idea for this, let’s first look at the first ransomware, and see what is done differently today.

The First Ransomware

The story of the first ransomware is an interesting one, to say the least. It was created by Joseph Popp, an evolutionary biologist from Harvard working on AIDS research in the 80s. The malware was distributed by mailing floppy discs to attendees to a WHO conference on, believe it or not, AIDS research (O’Kane et al., 2018). Popp was quickly found and arrested, and while no one knows why he did this, his idea led to the creation of an entirely new criminal enterprise in ransomware. In premise, Popp’s ransomware worked on the same basic principle of ransomware today: infect a computer, encrypt everything, and demand payment for it back, but there were several key differences between Popp’s ransomware and modern ransomware. For one, his ransom note (the message telling victims what is happening and what they should do about it) was primitive and nowhere near visually appealing. On top of that, payment was asked to be sent to a PO Box in Panama (O’Kane et al., 2018). There was no clear indication of who the money was being sent to, or how sending cash to Panama would allow one to get their files back. Put yourself in the shoes of a researcher in the 80s. You probably wouldn’t pay in this situation, right? 

Ransomware Today

These days, there are plenty of ransomware groups following in Popp’s footsteps taking various approaches to gaining trust. One ransomware group, Darkside, states plainly in their ransom note that they are here to make money, and if they did not return on their promise to decrypt their victim’s data, no one would pay them going forward (Smeets, 2025). This approach is backed by psychology. Studies have shown that, when marketing to a customer, companies that state their intentions plainly are more likely to gain trust (Dayal, 1999). This extends to the ransomware domain because ransomware operators are also trying to gain trust to receive payment. Another group, BlackCat, engages in anonymous “press releases” to clear up rumors about themselves. In this instance, various news outlets had misattributed attacks to BlackCat, and their “press release” attempted to clarify that they were not making any purely malicious attacks, their sole purpose was to make a deal and return the encrypted data (Smeets, 2025). Another vector of trust groups have exploited rests in User Interface (UI) design. Studies show that just changing the UI in a payment portal increased the number of users who shared their contact information by 31.3% (Yashimi, 2020). This demonstrated that interfaces based on familiar items or other interfaces makes people more likely to give up information (Yashimi et al., 2020). During Conti’s time operating as a ransomware group, they exploited this by using a professional looking ransom note, and sleek payment portals that made victims feel like they were completing a legitimate business transaction. There are many other strategies too, including providing 24/7 customer support, and referring to victims as “customers” (Smeets, 2025).

How Much Damage Does Ransomware Cause?

While it is hard to know exactly how much ransomware is costing year over year, many estimates exist. One estimate from the FBI placed ransomware at a market worth of 200 million USD, with one ransomware family (Cryptowall) totalling 18 million USD in losses in the US alone (O’Kane et al., 2018). How much ransomware operators are asking for in one attack varies by the operator and sometimes by the estimated worth of data encrypted. One company, Symantec, put average ransoms around 679 USD, with some as large as 6285 USD (O’Kane et al., 2018). This of course varies with the price of bitcoin, which is the most popular way for operators to demand payment (Huang et al., 2017). But who is paying these large ransoms? Turns out that, according to a study by Huang et al., 74.5% of Cerber (another family of ransomware) targets were residential. Because these numbers were based on internet service providers, this means that most targets used residential routers, so likely households. While the study is limited in scope due to these numbers being only related to one family of ransomware, these numbers are striking. While only somewhere between 1% and 3% of victims pay the ransom (O’Kane et al., 2018), one can easily see how lucrative the ransomware business is. 

 

Overall, the Ransomware Trust Paradox is a theory that I find fascinating, the idea that criminal enterprises engaging in explicitly untrustworthy activities are exploiting trust to meet their ends is something never seen in the criminal world before the introduction of ransomware. Tracing ransomware from its roots reveals the importance of this paradox in the operations of modern ransomware groups, and helps us think about how to better defend against them. Going forward, individuals and corporations can use this idea to make better value judgments when they are hit by ransomware, and better understand how to defend themselves against it. 

 

References

Dayal, S., Landesberg, H., & Zeisser, M. (1999). How to build trust online. Marketing Management, 8(3), 64. Retrieved from https://www.proquest.com/scholarly-journals/how-build-trust-online/docview/194194113/se-2

Huang, D. Y., Aliapoulios, M. M., Li, V. G., Invernizzi, L., Bursztein, E., McRoberts, K., Levin, J., Levchenko, K., Snoeren, A. C., & McCoy, D. (2018, May 1). Tracking Ransomware End-to-end. IEEE Xplore. https://doi.org/10.1109/SP.2018.00047

O’Kane, P., Sezer, S., & Carlin, D. (2018). Evolution of ransomware. IET Networks, 7(5), 321–327. https://doi.org/10.1049/iet-net.2017.0207

Shutterstock. (2024). Ransomware. In Ransomware explained: How it works and how to remove it.

Smeets, M. (2025). The Ransomware Trust Paradox. https://virtual-routes.org/wp-content/uploads/2025/04/Virtual-Routes-Pharos-Report-Series-No.-2.pdf

Yashmi, N., Momenzadeh, E., Taghipour Anvari, S., Adibzade, P., Moosavipoor, M., Sarikhani, M., & Feridouni, K. (2020). THE EFFECT OF INTERFACE ON USER TRUST; USER BEHAVIOR IN E-COMMERCE PRODUCTS. Proceedings of the Design Society: DESIGN Conference, 1, 1589–1596. https://doi.org/10.1017/dsd.2020.103

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  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.