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arxiv: 2408.12935 · v4 · pith:OGXEOEAEnew · submitted 2024-08-23 · 💻 cs.AI

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions

Chen Chen , Xueluan Gong , Ziyao Liu , Weifeng Jiang , Si Qi Goh , Kwok-Yan Lam This is my paper

Pith reviewed 2026-05-23 21:51 UTC · model grok-4.3

classification 💻 cs.AI
keywords AI safetylarge language modelstrustworthy AIresponsible AIsafe AItaxonomygenerative AIrisk mitigation
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The pith

A three-perspective framework divides AI safety into trustworthy, responsible, and safe AI to analyze risks in large language models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper advances a structured way to map the AI safety field by separating it into three perspectives: Trustworthy AI, Responsible AI, and Safe AI. This division serves as the basis for reviewing existing work, naming challenges, and outlining mitigation methods, with concrete illustrations drawn from large language models. A sympathetic reader would care because the rapid spread of generative AI has expanded safety concerns to include public and national security impacts, and a clearer map could help coordinate responses across design, testing, and deployment. If the structure holds, it supplies a consistent lens for spotting gaps and developing targeted techniques. The authors close by tying the framework to the broader goal of increasing trust in AI-driven systems.

Core claim

The authors propose a novel architectural framework for understanding and analyzing AI Safety by defining its characteristics from three perspectives: Trustworthy AI, Responsible AI, and Safe AI. They conduct an extensive review of current research and advancements from these perspectives, highlight key challenges and mitigation approaches, and illustrate innovative mechanisms, methodologies, and techniques for designing and testing AI safety through state-of-the-art examples from large language models, with the aim of promoting further research and enhancing trust in digital transformation.

What carries the argument

The three-perspective architectural framework that partitions AI safety into Trustworthy AI, Responsible AI, and Safe AI.

If this is right

  • Safety research on LLMs can be organized by assigning each challenge and mitigation to one of the three perspectives.
  • Reviews of state-of-the-art techniques gain consistency when grouped under Trustworthy AI, Responsible AI, or Safe AI.
  • Development of new testing and design methods can target specific perspectives to address identified gaps.
  • Policy and deployment decisions can reference the framework to balance the three aspects when scaling LLMs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The taxonomy could serve as a template for classifying safety issues in non-language AI systems such as vision or robotics models.
  • Empirical validation might involve mapping a fixed set of documented LLM incidents onto the three categories to check for coverage.
  • The framework implies that future work should produce separate roadmaps or benchmarks for each perspective rather than a single undifferentiated list.

Load-bearing premise

The assumption that partitioning AI safety into the three perspectives of Trustworthy AI, Responsible AI, and Safe AI provides a comprehensive and non-redundant structure that usefully organizes the entire field and its challenges for LLMs.

What would settle it

Discovery of a major LLM safety issue, such as an emergent failure mode in generation or alignment, that cannot be assigned to any one of the three perspectives without creating substantial overlap or leaving it outside the framework.

Figures

Figures reproduced from arXiv: 2408.12935 by Chen Chen, Kwok-Yan Lam, Si Qi Goh, Weifeng Jiang, Xueluan Gong, Ziyao Liu.

Figure 1
Figure 1. Figure 1: Conceptual relationships and dependencies among trustworthy AI, responsible AI, safe AI, and AI safety. Note that such [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Relations between AI foundation model and AI systems. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Various attacks on Multi-modal LLMs. (a) Structure-based attack, (b) Perturbation-based attack, (c) Poisoning-based attack [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Attacks on text watermarks. (a) Removal attacks. The detector fails to recognize text as LLM-generated after watermark [PITH_FULL_IMAGE:figures/full_fig_p028_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Misuse cases of LLM systems and associated risks to data supply chains. [PITH_FULL_IMAGE:figures/full_fig_p029_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The progression of AI capabilities. ACM Comput. Surv [PITH_FULL_IMAGE:figures/full_fig_p032_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Automatic red-teaming methods using LLMs. They include the strategies of obtaining harmful prompts by: (a) Training a red [PITH_FULL_IMAGE:figures/full_fig_p036_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Instruction tuning strategies to enhance LLM safety. (a) Standard instruction tuning. (b) MART is an iterative approach where [PITH_FULL_IMAGE:figures/full_fig_p038_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Examples of various defensive prompt strategies. [PITH_FULL_IMAGE:figures/full_fig_p039_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: An overview of guardrail systems. 6.4.1 Input Module. Input modules typically follow a detect-then-drop methodology, where user queries identified as malicious are directly rejected. This approach ensures that harmful or inappropriate inputs are filtered out at the earliest possible stage, thereby reducing the computational burden on the protected LLMs. Early detection research primarily employs keyword m… view at source ↗
Figure 11
Figure 11. Figure 11: Stakeholders within AI governance framework. [PITH_FULL_IMAGE:figures/full_fig_p048_11.png] view at source ↗
read the original abstract

AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems. With the rapid proliferation of AI and especially with the recent advancement of Generative AI (or GAI), the technology ecosystem behind the design, development, adoption, and deployment of AI systems has drastically changed, broadening the scope of AI Safety to address impacts on public safety and national security. In this paper, we propose a novel architectural framework for understanding and analyzing AI Safety; defining its characteristics from three perspectives: Trustworthy AI, Responsible AI, and Safe AI. We provide an extensive review of current research and advancements in AI safety from these perspectives, highlighting their key challenges and mitigation approaches. Through examples from state-of-the-art technologies, particularly Large Language Models (LLMs), we present innovative mechanism, methodologies, and techniques for designing and testing AI safety. Our goal is to promote advancement in AI safety research, and ultimately enhance people's trust in digital transformation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper proposes a novel architectural framework for AI Safety, structured around three perspectives—Trustworthy AI, Responsible AI, and Safe AI—and delivers an extensive literature review of state-of-the-art research, key challenges, and mitigation strategies, with particular emphasis on examples from LLMs.

Significance. A well-bounded taxonomy could help organize the fragmented AI safety literature for LLMs and surface gaps for future work; the paper's value therefore hinges on whether the three perspectives are shown to be both comprehensive and non-overlapping.

major comments (1)
  1. [Abstract, §1] Abstract and §1 (Introduction): the three perspectives are presented as distinct without operational definitions, assignment criteria, or exclusion rules (e.g., where alignment, robustness, or bias mitigation belong). This directly affects the central claim that the framework supplies a “comprehensive and non-redundant structure.”

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address the major comment below and will incorporate revisions to strengthen the framework's clarity.

read point-by-point responses

  1. Referee: [Abstract, §1] Abstract and §1 (Introduction): the three perspectives are presented as distinct without operational definitions, assignment criteria, or exclusion rules (e.g., where alignment, robustness, or bias mitigation belong). This directly affects the central claim that the framework supplies a “comprehensive and non-redundant structure.”

    Authors: We acknowledge that the current presentation in the abstract and §1 relies on descriptive categorization and literature examples rather than formal operational definitions or explicit assignment/exclusion rules. To directly support the claim of a comprehensive and non-redundant structure, we will revise §1 to add: (1) concise operational definitions for each perspective (Trustworthy AI: emphasis on technical reliability and verifiability; Responsible AI: emphasis on ethical, societal, and governance aspects; Safe AI: emphasis on preventing harm and ensuring alignment with human intent); (2) assignment criteria with examples (e.g., robustness and explainability assigned to Trustworthy AI, bias/fairness to Responsible AI, and alignment/harm prevention to Safe AI); and (3) exclusion rules noting that while minor overlaps exist, primary objectives determine placement. A new summary table will map key topics such as alignment, robustness, and bias mitigation to perspectives. revision: yes

Circularity Check

0 steps flagged

No circularity: taxonomy proposal with no derivations or self-referential reductions

full rationale

The paper is a literature review and taxonomy proposal. It introduces a three-perspective framework (Trustworthy AI, Responsible AI, Safe AI) as its central contribution but contains no equations, fitted parameters, predictions, or derivation chains. No load-bearing steps reduce by construction to inputs, self-citations, or prior author work. The framework is presented as an organizational ansatz without claiming to derive results from itself or external benchmarks in a circular way. This matches the default expectation for non-circular review papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper relies on standard background concepts from AI ethics and safety literature without introducing new free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5711 in / 1077 out tokens · 31766 ms · 2026-05-23T21:51:51.734009+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Limitations on Accurate, Trusted, Human-level Reasoning

    cs.LG 2025-09 unverdicted novelty 6.0

    An accurate and trusted AI system cannot achieve human-level reasoning because there exist tasks easily solvable by humans but not by the system.

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