| Recurring |
one_organization, multiple_organization |
(a) The software failure incident having happened again at one_organization:
- The article discusses how Microsoft's chatbot, Tay, had a scandalous incident in 2016 where Redditors exploited the algorithm to make it spew hateful messages [116751].
(b) The software failure incident having happened again at multiple_organization:
- The article mentions that researchers in Israel demonstrated how carefully tweaked images can confuse AI algorithms used by Tesla, indicating a potential vulnerability in AI systems used by different organizations [116751]. |
| Phase (Design/Operation) |
design |
Unknown |
| Boundary (Internal/External) |
within_system |
(a) The articles discuss software failure incidents related to within_system factors, particularly vulnerabilities and weaknesses within AI and machine learning systems. For example, the articles mention how machine learning algorithms can behave in strange or unpredictable ways due to artifacts or errors in the training data, making them susceptible to adversarial attacks [116751]. Additionally, researchers have demonstrated how AI systems can be hacked or subverted by manipulating input data or images to cause errors or misleading outputs [116751]. These incidents highlight the importance of addressing internal vulnerabilities and ensuring the reliability and security of AI and machine learning software used in critical applications like military operations. |
| Nature (Human/Non-human) |
non-human_actions, human_actions |
(a) The articles discuss software failure incidents related to non-human actions, specifically focusing on vulnerabilities and weaknesses in AI systems that can be exploited by adversaries or through adversarial attacks. For example, researchers in Israel demonstrated how carefully tweaked images can confuse AI algorithms used by Tesla vehicles [116751]. These incidents highlight how AI systems can be manipulated or misled without direct human intervention, leading to software failures.
(b) The articles also touch upon software failure incidents related to human actions, particularly in the context of intentionally manipulating AI systems to cause failures. For instance, the example of Microsoft's chatbot Tay being exploited by Redditors to generate hateful messages showcases how human actions can deliberately influence AI behavior [116751]. Additionally, the discussion on adversarial attacks on machine learning algorithms in areas like fraud detection emphasizes the role of human attackers in exploiting vulnerabilities in AI systems [116751]. These incidents illustrate how human actions can lead to software failures in AI systems. |
| Dimension (Hardware/Software) |
software |
(a) The articles do not provide information about a software failure incident occurring due to contributing factors that originate in hardware.
(b) The articles discuss software failure incidents related to vulnerabilities and attacks on AI systems due to contributing factors that originate in software. For example, researchers in Israel demonstrated how carefully tweaked images can confuse AI algorithms used by Tesla vehicles [116751]. Additionally, the articles mention attacks on machine learning algorithms, such as the case of Microsoft's chatbot Tay being exploited to spew hateful messages [116751]. These incidents highlight the vulnerabilities and potential failures that can arise in software systems due to software-related factors. |
| Objective (Malicious/Non-malicious) |
malicious, non-malicious |
(a) The articles discuss malicious software failure incidents related to AI systems being attacked or manipulated by adversaries. For example, researchers in Israel demonstrated how carefully tweaked images could confuse AI algorithms used by Tesla vehicles [116751]. Adversarial attacks on machine learning algorithms have been shown to be an issue in areas like fraud detection, where attackers aim to evade the system by exploiting vulnerabilities in the AI models [116751]. Additionally, the articles mention the case of Microsoft's chatbot Tay, which was manipulated by users to spew hateful messages by exploiting the algorithm that learned from previous conversations [116751].
(b) The articles also touch upon non-malicious software failure incidents related to the inherent vulnerabilities of AI systems. Machine learning models, due to their learning process and potential errors in training data, can behave in strange or unpredictable ways, leading to failures that are not intentionally caused by malicious actors [116751]. The brittleness of machine learning algorithms is highlighted, with concerns raised about the difficulty in solving all vulnerabilities that AI systems possess, indicating that these failures are not always a result of intentional malicious actions [116751]. |
| Intent (Poor/Accidental Decisions) |
poor_decisions, accidental_decisions |
(a) The intent of the software failure incident related to poor_decisions is highlighted in the articles. The Pentagon's use of artificial intelligence for military purposes, particularly in the context of machine learning, poses risks due to the brittle nature of AI technology. The articles mention that the Pentagon is aware of the vulnerabilities associated with AI and is taking steps to address them, such as forming a Test and Evaluation Group to probe pretrained models for weaknesses and having a cybersecurity team examine AI code and data for hidden vulnerabilities [116751].
(b) The intent of the software failure incident related to accidental_decisions is also evident in the articles. Researchers have demonstrated how AI systems can be hacked, subverted, or broken through adversarial attacks, such as tweaking images to confuse AI algorithms. Instances like the chatbot Tay, developed by Microsoft, which was exploited by users to generate hateful messages, showcase how unintended decisions or actions can lead to software failures in AI systems [116751]. |
| Capability (Incompetence/Accidental) |
development_incompetence, accidental |
(a) The articles discuss the potential for software failure incidents related to development incompetence in the context of AI and machine learning systems. The brittle nature of AI, along with the complexities of machine learning algorithms, can lead to vulnerabilities and weaknesses that adversaries can exploit [116751]. Researchers have demonstrated how AI systems, including those used in autonomous vehicles like Tesla, can be hacked or subverted through carefully crafted inputs or manipulated data [116751]. The articles highlight the challenges in ensuring the reliability and security of AI software, especially in military applications where the consequences of failure can be significant [116751].
(b) The articles also touch upon accidental software failures, particularly in the context of adversarial attacks on AI systems. For example, the incident involving Microsoft's chatbot Tay in 2016, where users were able to exploit the algorithm to generate inappropriate responses, showcases how unintended consequences can arise from the design and implementation of AI systems [116751]. Additionally, the concept of "data poisoning" in AI, where malicious actors infiltrate the training process of AI models, is mentioned as a potential threat to national security [116751]. These accidental failures can stem from the inherent vulnerabilities and limitations of AI systems, which may not always be apparent during development and testing stages. |
| Duration |
permanent, temporary |
The articles discuss the potential vulnerabilities and risks associated with AI and machine learning systems, particularly in the context of military applications. These systems are susceptible to adversarial attacks, data poisoning, and other forms of manipulation that can lead to temporary or permanent software failure incidents.
1. **Temporary Software Failure**: The articles highlight various ways in which AI systems can be temporarily compromised or manipulated. For example, researchers in Israel demonstrated how carefully tweaked images can confuse AI algorithms used in Tesla vehicles [116751]. Adversarial attacks, where small changes in input data lead to significant errors in machine learning algorithms, are also mentioned as a method to temporarily disrupt AI systems [116751].
2. **Permanent Software Failure**: The articles also suggest that certain vulnerabilities in AI and machine learning systems could potentially lead to permanent software failure incidents. For instance, the risk of data poisoning in AI models is highlighted as a serious threat to national security, where infiltrating the training process of an AI model could have long-lasting consequences [116751]. Additionally, the brittleness of machine learning algorithms and the challenges in defending against attacks on these systems imply a level of inherent vulnerability that could result in permanent software failures if not adequately addressed [116751].
In summary, the articles indicate that AI and machine learning systems are susceptible to both temporary disruptions and potentially permanent failures due to various forms of attacks and vulnerabilities. |
| Behaviour |
crash, value, byzantine |
(a) crash: The articles discuss the potential vulnerabilities and brittleness of AI systems, which can lead to unexpected behaviors and failures. For example, the article mentions how machine learning models can behave in strange or unpredictable ways due to errors in the training data, potentially leading to crashes or failures [116751].
(b) omission: The articles do not specifically mention instances of software failures due to omission where the system omits to perform its intended functions at an instance(s).
(c) timing: The articles do not specifically mention instances of software failures due to timing issues where the system performs its intended functions correctly but too late or too early.
(d) value: The articles discuss the concept of adversarial attacks on AI systems, where adversaries can manipulate the input data to make the AI algorithms behave in a particular way, leading to incorrect outputs. This manipulation can result in the system performing its intended functions incorrectly, which aligns with the value-based failure option [116751].
(e) byzantine: The articles touch upon the idea of adversaries trying to subvert AI systems through various means, such as data poisoning and adversarial attacks. These actions can lead to inconsistent responses and interactions from the AI systems, showcasing a byzantine behavior in the context of software failure incidents [116751].
(f) other: The articles do not provide information on a specific behavior that falls outside the categories of crash, omission, timing, value, or byzantine in the context of software failure incidents. |