| Recurring |
one_organization, multiple_organization |
(a) The software failure incident related to fooling AI systems with simple hacks has happened before with OpenAI's previous AI system, GPT-3. Both Clip and GPT-3, created by OpenAI, have shown vulnerabilities to attacks such as typographic attacks. OpenAI acknowledges that these weaknesses are a reflection of some underlying strengths of its image recognition system [112014].
(b) The article mentions a past incident involving Google in 2015 where the search engine automatically tagged images of black people as "gorillas." This incident highlighted the underlying issues with AI systems and the challenges in accurately categorizing images. The article also mentions that Google had not fully resolved the problem by 2018, indicating a recurring issue with AI systems across different organizations [112014]. |
| Phase (Design/Operation) |
design |
The software failure incident described in the articles is related to the design phase. The weakness in the AI system created by OpenAI, known as Clip, was due to a "typographic attack" where the AI could be fooled by simple hacks like pasting labels over images. This vulnerability was a result of exploiting the model's ability to read text robustly, indicating a flaw introduced during the design and development of the system [Article 112014]. |
| Boundary (Internal/External) |
within_system |
(a) The software failure incident described in the article is within_system. The weakness identified in the AI system created by OpenAI, known as Clip, is a result of a "typographic attack" where the AI can be fooled by simple hacks like pasting labels over images. This vulnerability is inherent to the design and functioning of the AI system itself, rather than being caused by external factors [112014]. |
| Nature (Human/Non-human) |
non-human_actions |
(a) The software failure incident in the article is related to non-human_actions. The weakness identified in the AI system created by OpenAI, known as Clip, is termed a "typographic attack." This attack involves fooling the AI model by exploiting its ability to read text robustly, such as pasting labels like "iPod" or dollar signs over images to mislead the system's recognition capabilities [Article 112014].
(b) The article does not mention any software failure incident related to human_actions. |
| Dimension (Hardware/Software) |
software |
(a) The software failure incident related to hardware:
- The article discusses a weakness in the AI system created by OpenAI called Clip, where it can be fooled by simple hacks like pasting labels over images. This weakness is referred to as a "typographic attack" and does not seem to be directly related to hardware failures [112014].
(b) The software failure incident related to software:
- The software failure incident discussed in the article is primarily related to the vulnerability of the AI system Clip to typographic attacks, where it can be tricked into misidentifying objects in images due to its design and algorithms. This failure originates in the software itself rather than hardware [112014]. |
| Objective (Malicious/Non-malicious) |
non-malicious |
(a) The software failure incident described in the articles is non-malicious. It involves a weakness in the artificial intelligence system created by OpenAI, known as Clip, which can be exploited through a "typographic attack." This attack involves fooling the AI model by pasting text over images, causing it to misclassify objects. OpenAI acknowledges this weakness as a reflection of some underlying strengths of its image recognition system, rather than a deliberate attempt to harm the system [112014]. |
| Intent (Poor/Accidental Decisions) |
poor_decisions, accidental_decisions |
(a) The intent of the software failure incident related to poor_decisions:
- The software failure incident described in the article is related to a weakness in the artificial intelligence system created by OpenAI called Clip. This weakness, known as a "typographic attack," allows the AI to be fooled by simple hacks involving pasting labels over images. OpenAI acknowledges that these attacks are not just an academic concern and can be exploited in the wild with minimal technology requirements like pen and paper [Article 112014].
(b) The intent of the software failure incident related to accidental_decisions:
- The software failure incident can also be attributed to accidental decisions or unintended consequences. The AI system's ability to read text robustly can sometimes lead to unintended outcomes, such as misclassifying images when text is added to them. This unintended consequence is a result of the AI's conceptual understanding of objects, which can sometimes fail to recognize important differences between categories, leading to accidental misclassifications [Article 112014]. |
| Capability (Incompetence/Accidental) |
development_incompetence |
(a) The software failure incident related to development incompetence is evident in the article as it discusses how the AI system, Clip, created by OpenAI, can be easily fooled by simple hacks like pasting labels over images. This weakness, termed a "typographic attack," highlights a vulnerability in the model's ability to read text robustly, leading to misclassifications of images [Article 112014].
(b) The software failure incident related to accidental factors is also apparent in the article. For example, Google's AI system had a significant issue in 2015 where it automatically tagged images of black people as "gorillas." This error was not intentionally introduced but was a result of underlying issues in the AI system's algorithms, leading to inappropriate tagging of images [Article 112014]. |
| Duration |
unknown |
The articles do not provide specific information about the duration of the software failure incident related to the typographic attacks on the AI system created by OpenAI. Therefore, it is unknown whether the failure was permanent or temporary. |
| Behaviour |
value, other |
(a) crash: The articles do not mention any instances of the software system crashing and losing its state.
(b) omission: The software failure incident described in the articles does not involve the system omitting to perform its intended functions at an instance(s).
(c) timing: The software failure incident does not relate to the system performing its intended functions too late or too early.
(d) value: The software failure incident is related to the system performing its intended functions incorrectly. Specifically, the AI system Clip incorrectly identifies objects when certain text is placed over images, leading to misclassifications [112014].
(e) byzantine: The software failure incident does not involve the system behaving erroneously with inconsistent responses and interactions.
(f) other: The behavior of the software failure incident can be categorized as a "misclassification" issue, where the AI system Clip misidentifies objects when certain text is placed over images, demonstrating a vulnerability in its image recognition capabilities [112014]. |