People who use Induction for deductive claims. How many fallacies is this?

I've seen statements of people arguing against medical science in peer reviewed Journals by saying:

Person 1: Vaccines might not work

Person 2: How so? What about the mountain of data from so many peer reviewed sources?

Person 1: Yeah that data might be falsified. 

Person 2 : So vaccines "might not work" because the data "might be" falsified? So your unproven claim rests on another unproven claim . How do you quantify the probability in something like that.  

Person 1 : Human beings have a tendency towards being corrupt.

Person 2. So your unproven/unfalsifiable claim rests on another unproven claim, with that claim resting on a red herring about humans "sometimes" do corruption? 

"That [vaccine efficacy] data might be falsified" - appeal to possibility. Yeah, it "might" be falsified. It might also be airtight. And considering the success of vaccine rollouts in the West in reducing transmission rates, I'd say it's closer to being airtight.

"Human beings have a tendency towards being corrupt" - so? This says nothing even if it were true, and is actually a non sequitur if they're using it to back up their previous claim. It reminds me of the very first post I made on this forum, where I asked about using general concepts (e.g. 'humans have a tendency towards corruption') to prove specific claims ('therefore Covid-19 vaccine data was fabricated').

As I understand the question, it relates to one participant suggesting that information presented in peer reviewed articles might not be true.  I'm not sure it's an issue of logic – rather, it seems more an opinion that results from one person wanting a different level of certainty than the other to accept information.

Person 1 claims that data in some articles submitted for peer review (and for articles that actually pass peer review) might be falsified.  If I had to classify that claim as true or false, I'd have to go with true – in fact, over recent years, there have been more than a small number of instances where such was demonstrated to be the case, even for what are seen as prominent journals and researchers.  

Person 2 seems to imply that some data in some articles might not be falsified.  Again, if I had to classify that claim as true or false, I'd have to go with true.

So, with two opposite claims showing up as likely true, I'm left wondering if it's more likely that a mass of data on a particular subject has been falsified or not.  If my experience is that these data tend to get falsified very often, I'm likely to lean toward doubting the peer review process; if my experience is that falsification of such data happens very infrequently, I'm likely to lean toward accepting the data.  My decision to accept or reject the data isn't a logical decision, it's based on what my experience tells me can be believed.

If pressed to turn it into a logical matter, I'd have to go with something like this:

** If the data in peer reviewed sources are true, then I accept the conclusions of those peer reviewed sources.  (If A, then B.)

** I believe (or don't) that the data are true.

** Therefore, I accept (or reject) the conclusions.

However, my acceptance or rejection of the data isn't a logical issue, it's a matter of how I assess the premise (that the data are accurate).  In other words, my decision will be based on how likely I believe it to be that the data are correct, or not.