Unpacking AI Security Considerations
Bibliographic record
Abstract
The field of Artificial Intelligence has emerged as a convincing tool to be used in a myriad of applications like finance, traffic prediction, health and travel sectors. Due to the enormous benefits provided in terms of automation, convenience, processing time, reduced manhours, and productivity, AI is being seen as the next technical revolution. AI is being showcased as a useful tool to stimulate creativity as well as provide support with its tremendous computational power. The release of tools like ChatGPT has exploded onto the technological scene. Users are making use of Large Language Models (LLMs) and tools to perform a host of activities like writing an essay, translating documents, and finding travel plans. However, the popularity of these tools has not been without risk. In the technology marketplace, the race to dominance can force competitors to waive safety concerns in favour of product adoption. Many are unaware of the potential dangers and risks that may inherently reside within AI tools. This paper looks at the potential risks of AI tools such the creation of misinformation or scams. AI security has now become a paramount concern that should not be ignored. In this paper, the potential risks and threat vectors of Artificial Intelligence will be covered. The aim will be to provide insight into the malicious use of Artificial Intelligence Tools through a discussion of techniques to bypass security controls. The paper aims to provide a more detailed account on how AI can be manipulated in order to empower users about the latest attack schemes.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".