The Project CASSANDRA director rejected an initial 
proposal to migrate the entire CASSANDRA codebase to 
the open-source Theano machine learning platform to 
facilitate onboarding the large number of new hires. 
Nonetheless, after discussions with Foundation HR, a 
compromise was reached where a new Foundation- 
proprietary machine library similar to an existing open- 
source platform was created for the project. Other 
unrelated Foundation projects took a different approach, 
and to prevent confusion with the open-source Apache 
Cassandra noSQL database being integrated into some 
of them at the time, Project CASSANDRA was renamed 
to Project MEDUSA. 


+ Addendum 3334-3 


Addendum 3334-3: With Project MEDUSA having 
repeatedly achieved 99.99% validation accuracy, on the 


recommendation of the team leads, project director Dr. 
Vukovié decided to retire Protocol 10-Kempelen. Instead, 
the MEDUSA network would add the VMC hazards it 
flags directly into its own training and validation data 
pool, SCP-3334-1 - the project stakeholders rationalized 
that the network was accurate and robust enough to 
tolerate the tiny amount of label noise that would 
subsequently be introduced. 


+ Incident 3334-1 


Incident 3334-1: During the week of / /20 , the 
monitored real-world accuracy of the MEDUSA model 
decreased at an alarming pace for several days, with a 
roughly corresponding increase in the number of 
containment breaches and new VMC hazard outbreaks. 
On / /20 , the real-world accuracy dipped to 87% anda 
MEDUSA failure event was declared. Both SCP-3334-2 
and the codebase were reverted to the last checkpoint. 
Nonetheless, even after this reversion, real world 
performance still lagged. 


The Project MEDUSA Testing Team, initially suspecting 
the automation of SCP-3334-1 element collection, 
reinstated Protocol 10-Kempelen to manually review 
every new VMC hazard added to SCP-3334-1 since the 
automation policy was put in place. The review 
uncovered approximately 15000 images of Yuno Gasai, 
the main character of the Japanese animation Mirai 
Nikki, in various forms incorporated into SCP-3334-1. 
Nearly all possessed anomalously memetic, albeit for the 
most part extremely minor, effects. The Implementation 
Team attempted to modify the neural net to recognize 
this common feature for special inspection, but found the 
network unable to identify these instances. Two days into 
the MEDUSA failure event, in the face of a mounting and 
non-trivial number of VMC containment breaches and 
outbreaks, the Director of the Department of Analytics 
demanded a status report. At this point Mobile Task 
Force Mu-4 (“Debuggers”) was brought in. 


Ensembling the neural networks improved the 
classification accuracy to around 88% as a temporary 
measure. Eventually, MTF Mu-4 proposed using 
principal components analysis and a prior cascade 
classifier placed before the main MEDUSA classifier to 
detect and remove malicious adversarial examples. This 
development removed the adversarial examples 
poisoning the MEDUSA network, restoring normal 
function. Protocol 10-Kempelen to vet potential training 
and validation examples of VMCs was restored. 


The MEDUSA failure event resulted in containment 
breaches and the outbreaks, of various sizes, of new 
VMC hazards. The incident required approximately 
thousand amnesticizations and caused _ casualties, 
including Foundation personnel. In total, the failure 
event cost the Foundation $ million dollars in damages, 
containment costs, and lost productivity. In its annual 
review, the O5 Council asked the Department of 
Analytics to submit a plan detailing steps taken by 
Project MEDUSA to avoid a similar disruption. 


Despite strong suspicions, to this day the Foundation 
has been unable to definitively assign blame for Incident 
3334-1 on any particular Gol. 


+ Email Communications Regarding Incident 3334-1 


From: Vladimir Vukovié [pcs.scitylana| 
kuvv#pcs.scitylana|kuvv] 

To: Dean Ackermann [pcs.4um.ftm| 
nnamrekcad#pcs.4um.ftm|nnamrekcad] 
Subject: Fixing MEDUSA 


Dr. Ackermann, 


Would you mind having Mu-4 take a look at 
MEDUSA for a bit? It's quite urgent. We can't 
make heads or tails of the current issue - we're 
used to dealing with anomalies, of course, but 
the recent 15k 3334-1 instances with the 


cartoon girl have been perplexing my 
Implementation Team - the network will 
correctly identify them as visual memetic 
anomalies, but it can't otherwise find the 
obvious commonality between them. It's a very 
minor memetic effect, to be sure, but I'm sure 
it's a symptom of the larger problem. 


Somebody on the Theory Team said you 
worked on containing a similar anomaly in the 
past, so maybe we'll have some luck here 
again. 


Regards, 
Dr. Vukovic 
Project MEDUSA Director 


From: Dean Ackermann [pcs.4um.ftm| 
nnamrekcad#pcs.4um.ftm|nnamrekcad] 
To: Vladimir Vukovié [pcs.scitylana| 
kuvv#pcs.scitylana|kuvv] 

Cc: Mary Wang [pcs.4um.ftm| 
gnawm#pcs.4um.ftm|gnawm] 

Subject: re: Fixing MEDUSA 


Hey Dr. Vukovic, 


No prob on priority, all the other project and 
containment teams have been submitting 
tickets about MEDUSA being broken anyway. 


Very interesting. | think your Theory guy was 
probably talking about SCP-2223. | can see 
the similarities between those two problems, in 
fact I'd guess they're both Celeramis's work 
(we pinned the Mirai loT malware on them last 
year in fact, seems they have a thing for 
anime). However, | mainly do traditional 
algorithms not all this hot deep learning stuff. 
I'll cc Mary to take a look, she worked at 
Google DeepMind so she should know a thing 


or 2. She also worked on 2223 with me if that's 
relevant. 


Best, 


Dean 
MTF Mu-4 Debugger 


From: Mary Wang [pcs.4um.ftm| 
gnawm#pcs.4um.ftm|gnawm] 

To: Vladimir Vukovié [pcs.scitylana| 
kuvv#pcs.scitylana|kuvv] 

Cc: Dean Ackermann [pcs.4um.ftm| 
nnamrekcad#pcs.4um.ftm|nnamrekcad], 
Samhita Reddy [pcs.4um.ftm| 
ydderhs#pcs.4um.ftmlydderhs], Achmed 
Hafizyar [pcs.4um.ftm|zifah#pcs.4um.ftm| 
zifah], Kelly Fitzgerald [pcs.scitylana| 
ztif#pcs.scitylana|ztif] 

Subject: re: Fixing MEDUSA 


To all, 


Jeez, what a mess. As a deep learning 
researcher myself | would have never 
imagined the Foundation would entrust so 
many critical operations to such a fickle 
machine learning algorithm like MEDUSA. 


| partly disagree with Dean, beyond the 
superficial similarities these are two totally 
different technical problems. 2223 was totally 
anomalous through and through, even pixel- 
by-pixel and normal stuff like SIFT didn't work 
On it. On the other hand "state of the art" deep 
learning Al is stupid and easily fooled. Maybe 
your 15k images are like 2223, idk, but | doubt 
it, anomalous engineering like that takes real 
work and across that many images? 


There's actually a totally scientific, non- 


anomalous explanation for your issue, it's 
called adversarial images (just to be clear, the 
images you're talking about just happen to be 
anomalous memes). Let me attach a pic from 
the Szegedy paper'4. You and a typical 
convolutional neural net will agree the left is a 
schoolbus, as it should be. But add some 
strategic noise, as indicated in the middle, on 
top and you get the right image. It's still a 
schoolbus, but most neural nets will now think 
it's an ostrich. Cheap non-anom trick to screw 
up dumb-as-rocks deep neural nets. 


Long story short somebody (agree with Dean 
here, it's probably Celeramis) is deliberately 
feeding in garbage to throw off MEDUSA's 
predictions. You'll notice that the anomalous 
memetic effects of the 15k weird images are 
extremely weak. These things are basically 
"barely memes" designed to deform our 
decision boundary and confuse the network 
trying to tell VMCs apart from normal pics. Put 
in less technical terms for you, imagine 
painting a bunch of apples orange and then 
teaching a little kid they were oranges. 


Let me think up a more permanent solution. 
cc'ing Achmed and Samhita for that. In the 
meantime just copy/paste the net a bunch of 
times and make an ensemble, sounds too 
good to be true but that'll reduce the variance 
and help a bit with the error. We're at what, 
85% right now? That might be enough to get 
us out or close enough this failure event. cc'ing 
Dr. Fitzgerald from your Implementation Team 
for that. 


Dr. Wang 
MTF Mu-4 "Debuggers" Operative, Al Division 


Attachment: 


From: Vladimir Vukovié [pcs.scitylana| 
kuvv#pcs.scitylana|kuvv] 

To: Mary Wang [pcs.4um.ftm| 
gnawm#pcs.4um.ftm|gnawm] 

Cc: Dean Ackermann [pcs.4um.ftm| 
nnamrekcad#pcs.4um.ftm|nnamrekcad], 
Samhita Reddy [pcs.4um.ftm| 
ydderhs#pcs.4um.ftm|ydderhs], Achmed 
Hafizyar [pcs.4um.ftm|zifah#pcs.4um.ftm| 
zifah], Kelly Fitzgerald [pcs.scitylana| 
ztif#pcs.scitylana|ztif] 

Subject: re: Fixing MEDUSA 


Dr. Wang, 


Thank you so much Dr. Wang. | think 
Implementation found that explanation very 
helpful, I'll tell Dr. Fitzgerald to get on what you 
suggested. 


| do have to agree that MEDUSA's stability 
leaves much to be desired, but I'm afraid we 
don't really have an alternative option right 
now. There's just so much to review on the 
internet and so many requests from within the 
Foundation that we really can't go back to the 
days when we just threw D-class at these 
VMCs. 


Regards, 
Dr. Vukovic 
Project MEDUSA Director 


+ Incident 3334-2 (Ongoing) 


Incident 3334-2 (Ongoing): Since / /20 , the real- 
world accuracy of Project MEDUSA has behaved 
erratically, often dipping below the optimal 99.9% with an 
average around 95%. On 04/ /20 in particular, the 


accuracy fell below the 90% threshold to a staggeringly 
low 71% accuracy, triggering a MEDUSA failure event as 
well as containment breaches and VMC hazard 
outbreaks, although it recovered the next day and has 
continued to stay above 90% since. Standard 
SCP-3334-2 and codebase reversions had no effect, or 
even worsened it. A slightly higher incidence of 
containment breaches and VMC hazard outbreaks has 
been associated with this deteriorated performance. The 
cause of this performance drop is as-yet unknown, 
despite active research by the Testing Team and MTF 
Mu-4. 


+ Email Communications Regarding Incident 3334-2 


From: Vladimir Vukovié [pcs.scitylana| 
kuvv#pcs.scitylana|kuvv] 

To: Mary Wang [pcs.4um.ftm| 
gnawm#pcs.4um.ftm|gnawm] 
Subject: re: MEDUSA again 


Dr. Wang, 


Sorry to bother you about this again, has your 
team made any progress? The Department of 
Analytics higher-ups want to know what's 
going on. 


Regards, 
Dr. Vukovic 
Project MEDUSA Director 


From: Mary Wang [pcs.4um.ftm| 
gnawm#pcs.4um.ftm|gnawm] 

To: Vladimir Vukovié [pcs.scitylana| 
kuvv#pcs.scitylana|kuvv] 

Subject: re: MEDUSA again 


To Dr. Vukovic, 


Sorry for the delay, it's just Mu-4 is attached to 


but the water is otherwise potable. 


The exact size of the labyrinth complex to which SCP-432 connects 
cannot be accurately measured as each time the doors of the 
cabinet are closed and then reopened the 'entrance' created by the 
cabinet apparently moves to a different section of the maze. 


The fate of personnel within the maze when the door is closed is 
unknown, although remains discovered within the maze suggest 
starvation is a likely outcome. Other remains, coupled with additional 
evidence gathered during exploration, suggests that the labyrinth 
contains a large predatory inhabitant of indeterminate species, 
hereafter known as SCP-432-1. 


GPS units used within SCP-432 are rendered useless, as are 
cellular phones. Remote-controlled devices sent into SCP-432 are 
similarly impaired and cease to function after travelling an average 
of 20 meters into the maze, rendering remote mapping of the 
internal layout impossible. High-gain radio transmissions can be 
used to keep in contact with personnel within the labyrinth, although 
significant interference occurs deeper into the maze. If the doors of 
the cabinet are closed then all forms of contact with personnel within 
SCP-482 are severed. 


Additional notes: SCP-432 was discovered in an abandoned 
industrial complex in , UK. It came to the attention of the 
Foundation after Dr. T. Small heard reports of several homeless 
persons in the area disappearing after staying in the complex. Upon 
investigation Dr. Small discovered the cabinet at the centre of an 
abandoned steel mill, surrounded by a number of sleeping bags, 
bags of clothing and other personal effects suggesting a number of 
homeless persons had recently made camp there. SCP-432 was 
unlocked, but the door closed upon discovery. After exploring the 
immediate area beyond the entrance, Dr Small exited SCP-432 and 
summoned Foundation personnel to transport the cabinet to 
Sector-25 for analysis. 


Currently expeditions have been sent into SCP-432 to attempt to 
chart its internal geography. To date D-class personnel have been 
lost within the maze. No further expeditions may be made without 
express permission of at least two (2) Level 4 Personnel. 


a lot of different SCPs, but we always have at 
least one guy looking into this rn. We really 
can't figure it out. It could be the types of 
VMCs out in the wild have drastically changed, 
or something is just ever so slightly jacked in 
some of the millions of weights of MEDUSA, or 
there's a hyperparameter among dozens that 
could be fine-tuned just a bit more. You guys 
are using auto-tuning for those hyperparams 
right? | think somebody in Analytics is 
developing something like that but for an entire 
neural net, whenever it's ready you could start 
over and try that and see if it gets rid of 
whatever kink is hidden in your model. I'll let 
you know if we make any progress on our end 
too. 


The only thing I'm worried about is one of the 
first things you learn in machine learning is the 
bias-variance tradeoff. Essentially you can 
either make an Al that performs really well in 
the lab, or not that much worse in the real 
world, but not both. Generally the more 
complicated you make a model, the more you 
reduce bias at the cost of increasing variance - 
the more detailed the model, the more it loses 
sight of the bigger picture. 


Every time we've had an issue with MEDUSA 
or we want to improve it our reaction has 
always been to slather more complicated math 
on top of whatever we have already to fix it. 
Then it'll Keep chugging along until it starts to 
randomly, unpredictably fail in the real world 
with all those real scips and real people relying 
on it like it did on 04/ /20 . And it'll do that 
more often the more "state of the art" 
algorithms we slap onto it. 


Dr. Wang 


MTF Mu-4 "Debuggers" Operative, Al Division 


Footnotes 

1. In this context, SaaS refers to having users remotely interact with 
a centrally hosted program rather than distributing individual copies 
to each. 

2. Miyamizu, M., Vogel, S., Hsiao, C., et al. (2001). scipDB: A highly- 
available secure NoSQL database for sensitive Foundation data and 
MCl-hazards. Terminal: An SCP Foundation Journal, 10(34), 42-56. 
Retrieved fromSCP Foundation Research Database. (Accession No. 
2335245) 

3. Containment specialists should refer toscipDB Documentationfor 
further details 

4. Hellian, F., Miyamizu, M., Vogel, S., et al. (1999). The Foundation 
Scalable File System: Organizing the Foundation’s cloud. Terminal: 
an SCP Foundation Journal, 8(24), 34-50. Retrieved fromSCP 
Foundation Research Database. (Accession No. 3654253) 

5. Containment specialists should refer toFSFS Documentationfor 
further details 

6. Goldberg, S., Recht, P. (1961). Feature engineering for soft- 
margin hyperplanes to safely identify cognitohazards. Terminal: an 
SCP Foundation Journal, 2(12), 10-23. Retrieved fromSCP 
Foundation Research Database. (Accession No. 7453535)