Hacking Next Gen ATMs: From Capture to Cashout
Bibliographic record
Abstract
MV (Chip & Pin) card ATM's are taking over the industry with the deadlines passed and approaching the industry rushes ATM's to the market. Are they more secure and hack proof? Over the past year I have worked at understanding and breaking the new methods that ATM manufactures have implemented on production ‘Next Generation’ Secure ATM systems. This includes bypassing Anti-skimming/Anti-Shimming methods introduced to the latest generation ATM's. along with NFC long range attack that allows real-time card communication over 400 miles away. This talk will demonstrate how a $2000-dollar investment criminals can do unattended ‘cash outs’ touching also on failures of the past with EMV implementations and how credit card data of the future will most likely be sold with the new EMV data having such a short life span. With a rise of the machines theme demonstration of ‘La-Cara’ and automated Cash out machine that works on Current EMV and NFC ATM's it is an entire fascia Placed on the machine to hide the auto PIN keyboard and flash-able EMV card system that is silently withdrawing money from harvested card data. This demonstration of the system can cash out around $20,000/$50,000 in 15 min. Bio: 11 Years Pen-testing, 12 years’ security research and programming experience. Working for a security Company in the Midwest Weston has recently Spoken at DEF CON 22 & 23, Black Hat USA 2016, Enterprise Connect 2016 ISC2-Security Congress, SC-Congress Toronto, HOPE11, BSIDES Boston and over 50 other speaking engagements from telecom Regional events to University’s on security subject matter. Working with A Major University’s research project with Department of Homeland Security on 911 emergency systems and attack mitigation. Attended school in Minneapolis Minnesota. Computer Science and Geophysics. Found several vulnerabilities’ in very popular software and firmware. Including Microsoft, Qualcomm, Samsung, HTC, Verizon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".