that countermeasures be employed in the design to prevent this type of attack. Also, improper implementation of PUF could introduce "backdoors" to an otherwise secure system. In June 2012, Dominik Merli, a scientist at Fraunhofer Research Institution for Applied and Integrated Security (AISEC) further claimed that PUF introduces more entry points for hacking into a cryptographic system and that further investigation into the vulnerabilities of PUFs is required before PUFs can be used in practical security-related applications. The presented attacks are all on PUFs implemented in insecure systems, such as FPGA or Static RAM (SRAM). It is also important to ensure that the environment is suitable for the needed security level, as otherwise attacks taking advantage of temperature and other variations may be possible. In 2015, some studies claimed it is possible to attack certain kinds of PUFs with low-cost equipment in a matter of milliseconds. A team at Ruhr Universität of Bochum, Germany, demonstrated a method to create a model of XOR Arbiter PUFs and thus be able to predict their response to any kind of challenge. Their method requires only 4 CRPs, which even on resource-constrained devices should not take more than about 200ms to produce. Using this method and a $25 device or an NFC-enabled smartphone, the team was able to successfully clone PUF-based RFID cards stored in the wallet of users while it was in their back pocket. Provable machine learning attacks The attacks mentioned above range from invasive, e.g., to non-invasive attacks. One of the most celebrated types of non-invasive attacks is machine learning (ML) attacks. From the beginning of the era of PUFs, it has been doubted if these primitives are subject to this type of attacks. In the lack of thorough analysis and mathematical proofs of the security of PUFs, ad hoc attacks against PUFs have been introduced in the literature. Consequently, countermeasures presented to cope with these attacks are less effective. In line with these efforts, it has been conjectured if PUFs can be considered as circuits, being provably hard to break. In response, a mathematical framework has been suggested, where provable ML algorithms against several known families of PUFs have been introduced. Along with this provable ML framework, to assess the security of PUFs against ML attacks, property testing algorithms have been reintroduced in the hardware security community and made publicly accessible. These algorithms trace their roots back to well-established fields