| Recurring |
unknown |
The articles do not provide information about a specific software failure incident happening again at a particular organization or across multiple organizations. Therefore, the information related to the recurrence of a software failure incident is unknown based on the provided articles. |
| Phase (Design/Operation) |
design, operation |
(a) The software failure incident related to the design phase can be seen in the article where researchers developed an algorithm that could reveal a person's passcode using data from six smartphone sensors. The algorithm was trained with data collected from three people entering random sets of four-digit PIN numbers on a phone, which recorded their entries. This algorithm was able to crack all 10,000 possible combinations of four-digit PINs by analyzing sensor data related to how the phone was tilted and how much light was blocked by the user's fingers [66205].
(b) The software failure incident related to the operation phase can be inferred from the article where researchers expressed concerns about the open access to the six sensors in smartphones, which could be accessed by any apps without authorization. They warned that this could lead to malicious apps collecting data from users over time to learn their behaviors and potentially launch attacks to guess PINs. This highlights a failure in the operation phase where unauthorized access to sensitive sensor data could compromise user security and privacy [66205]. |
| Boundary (Internal/External) |
within_system |
(a) within_system: The software failure incident described in the article is primarily within the system. The failure occurred due to the exploitation of vulnerabilities within the smartphone sensors and the algorithm used to analyze the sensor data. Researchers developed an algorithm that could accurately guess a user's PIN by analyzing data from six smartphone sensors, including the accelerometer, gyroscope, magnetometer, proximity sensor, barometer, and ambient light sensor. This algorithm was trained using data collected from individuals entering PIN numbers, allowing it to successfully crack all 10,000 possible combinations of four-digit PINs [66205]. The failure stemmed from the inherent design and functioning of the sensors and the algorithm, highlighting an internal system vulnerability that could be exploited by malicious actors. |
| Nature (Human/Non-human) |
non-human_actions |
(a) The software failure incident in the article is related to non-human actions. The failure occurred due to the vulnerability in smartphone sensors that allowed a new algorithm to correctly guess a person's PIN using data from six sensors without human participation. The sensors, including accelerometer, gyroscope, magnetometer, proximity sensor, barometer, and ambient light sensor, were used to gather information needed to crack the PIN and access personal information [66205]. |
| Dimension (Hardware/Software) |
hardware, software |
(a) The software failure incident related to hardware:
The article discusses a software failure incident that is related to hardware. Researchers developed an algorithm that exploits data from six smartphone sensors (accelerometer, gyroscope, magnetometer, proximity sensor, barometer, and ambient light sensor) to guess a user's PIN with high accuracy. The sensors are built into the phone hardware and are used to gather information needed to crack the PIN, indicating that the failure is due to contributing factors originating in the hardware [66205].
(b) The software failure incident related to software:
The software failure incident discussed in the article is primarily related to software. The researchers developed an algorithm based on deep learning that analyzes data from the smartphone sensors to guess PIN numbers accurately. The algorithm's success in cracking PINs is attributed to the software's ability to interpret sensor data and learn user behavior patterns over time. Additionally, the concern raised is about malicious apps exploiting the software to collect sensor data and launch attacks on user security, highlighting the software-related vulnerabilities [66205]. |
| Objective (Malicious/Non-malicious) |
malicious |
(a) The software failure incident described in the article is malicious in nature. Researchers developed an algorithm that leverages data from smartphone sensors to help hackers correctly guess a person's PIN with high accuracy. The algorithm was trained using data collected from individuals entering random sets of four-digit PIN numbers, and it can potentially crack any of the 10,000 possible combinations of four-digit PINs. The concern raised is that this technique could be exploited by malicious apps to spy on user behavior and compromise security ([66205]). |
| Intent (Poor/Accidental Decisions) |
poor_decisions |
The intent of the software failure incident reported in the article is related to poor_decisions. The incident involves researchers developing an algorithm that can potentially help hackers guess a person's PIN using data from smartphone sensors. The algorithm was trained with data collected from individuals entering random sets of four-digit PIN numbers, which raises concerns about privacy and security implications. Additionally, the researchers highlighted the potential risks associated with open access to smartphone sensors, allowing malicious apps to gather sensitive information without user authorization [66205]. |
| Capability (Incompetence/Accidental) |
unknown |
(a) The software failure incident related to development incompetence is not applicable in this case as the article does not mention any failure occurring due to lack of professional competence by humans or the development organization.
(b) The software failure incident related to an accidental failure is not applicable in this case as the article does not mention any failure occurring due to contributing factors introduced accidentally. |
| Duration |
unknown |
The articles do not provide information about a software failure incident being either permanent or temporary. |
| Behaviour |
other |
(a) crash: The software failure incident described in the article does not involve a crash where the system loses state and does not perform any of its intended functions. The incident is more focused on a security vulnerability related to the sensors in smartphones that could potentially allow hackers to guess a user's PIN [66205].
(b) omission: The software failure incident does not involve a failure due to the system omitting to perform its intended functions at an instance(s). Instead, it highlights a security flaw in the system related to the data collected by smartphone sensors that could be exploited by hackers to guess PIN numbers [66205].
(c) timing: The software failure incident is not related to a timing failure where the system performs its intended functions but does so too late or too early. The focus of the incident is on the potential security risk posed by the data collected from smartphone sensors that could be used to guess PIN numbers [66205].
(d) value: The software failure incident does not involve a failure due to the system performing its intended functions incorrectly. Instead, it highlights a security vulnerability in the system related to the data collected by smartphone sensors that could be leveraged by hackers to guess PIN numbers [66205].
(e) byzantine: The software failure incident is not characterized by a byzantine failure where the system behaves erroneously with inconsistent responses and interactions. The incident primarily revolves around a security vulnerability in smartphone sensors that could be exploited by hackers to guess PIN numbers [66205].
(f) other: The behavior of the software failure incident can be categorized as a security vulnerability related to the exploitation of smartphone sensors to potentially guess a user's PIN number. This behavior falls under the category of a security breach or vulnerability rather than a traditional software failure like a crash or omission [66205]. |