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Foundational Challenges in Assuring Alignment and Safety of Large Language Models

Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, Jose Hernandez-Orallo, Lewis Hammond, Eric Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Aleksandar Petrov, Christian Schroeder de Witt, Sumeet Ramesh Motwan, Yoshua Bengio, Danqi Chen, Philip H. S. Torr, Samuel Albanie, Tegan Maharaj, Jakob Foerster, Florian Tramer, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger

arXiv:2404.09932v2cs.LGcs.AIcs.CLcs.CY

TL;DR

Foundational challenges in assuring LLM safety and alignment remain insufficiently addressed across scientific, development, deployment, and sociotechnical domains. The paper discusses 18 foundational challenges in LLM safety and alignment alongside 200+ concrete research questions for further investigation. The agenda identifies 18 foundational challenges and provides 200+ open-ended research questions roughly sized for graduate theses.

  • Problem

    Foundational challenges in assuring LLM safety and alignment remain insufficiently addressed across scientific, development, deployment, and sociotechnical domains.

    Source: §1.1 · p. 7

  • Method

    The paper discusses 18 foundational challenges in LLM safety and alignment alongside 200+ concrete research questions for further investigation.

    Source: §1.1 · p. 7

  • Results

    The agenda identifies 18 foundational challenges and provides 200+ open-ended research questions roughly sized for graduate theses.

    Source: Machine Learning and NLP Researchers · p. 6, §1.1 · p. 7

  • Takeaways & Limitations

    The agenda offers junior and new researchers starting points for selecting promising, actionable research directions without attempting a comprehensive survey.

    Source: Machine Learning and NLP Researchers · p. 6

    The agenda is not exhaustive and does not catalog all known or unknown challenges in assuring LLM-based systems’ safety and alignment.

    Source: §5.1 · p. 110

Abstract

from arXiv · show

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose $200+$ concrete research questions.

Reader’s Guide

The document offers flexible reading strategies for a lengthy agenda, while targeting researchers seeking actionable LLM safety and alignment directions and making sociotechnical challenges accessible to a wider audience.

Reader’s Guide

  • Readers should begin with Section 1, then browse Sections 2–4 and Tables 1, 3, and 4 before pursuing self-contained challenges in any order.This strategy provides a high-level overview followed by interest-driven deep dives.Source: Reader’s Guide · p. 6

Machine Learning and NLP Researchers

  • The agenda primarily serves technical researchers, especially junior or newly entering researchers, by presenting 200+ dissertation-scale research questions with motivation, background, related work, and future directions.It assumes only general knowledge of LLMs, their architecture, and training, but is not intended as a comprehensive survey.Source: Machine Learning and NLP Researchers · p. 6
  • Many identified challenges also offer technically and scientifically interesting LLM research problems for readers whose primary motivation is not safety.The document therefore supports both safety-focused researchers and those seeking broader LLM research directions.Source: Machine Learning and NLP Researchers · p. 6

Sociotechnical Researchers and Other Stakeholders

  • Section 4 treats LLMs as sociotechnical systems, maps its challenges to contributing fields, and is designed for a wider audience with only high-level LLM familiarity.It emphasizes that LLM safety requires deep consideration of sociotechnical factors.Source: Sociotechnical Researchers and Other… · p. 6

1 Introduction

LLMs’ scale, capabilities, and societal impact make safety and alignment unusually difficult, while technical tools and sociotechnical structures remain insufficient. The paper maps 18 foundational challenges across scientific understanding, development and deployment methods, and sociotechnical issues, posing 200+ research questions.

1 Introduction

  • LLMs’ expansive scale, broad capabilities, and rapidly growing applications increase both the difficulty of assurance and the potential for societal harm.These challenges are recognized across private laboratories, governments, and research communities.Source: §1 · p. 7

1.1 Why This Agenda?

  • The agenda extends prior AI-safety work by addressing novel LLM-specific challenges with broader coverage than narrowly objective-misspecification-focused treatments, including Kenton et al. (2021).The authors characterize it as the most comprehensive and detailed treatment of LLM alignment and safety challenges to date.Source: §1.1 · p. 7
  • The paper identifies 18 foundational, unambiguous, researchable challenges whose resolution is important for assuring LLM and derivative-system safety and alignment, alongside 200+ concrete research questions.The questions are open-ended and generally intended to support graduate-thesis-sized investigations.Source: §1.1 · p. 7

1.2 Terminology

  • Alignment concerns whether an LLM tries to follow its developer’s intent, safety concerns unplanned undesirable harms in context, and assurance concerns evidence for either property.Assurance may include scientific understanding, behavioral evaluation, explanations, and responsible development practices; alignment does not guarantee intended behavior or safety.Source: §1.2 · p. 8

1.3 Structure

  • Scientific-understanding challenges target principled prediction of capabilities, especially in-context learning, reasoning, agents, multi-agent risks, and safety-performance trade-offs.Development and deployment challenges cover pretraining, finetuning, evaluation, interpretability, jailbreaks, prompt injection, and data poisoning.Source: §1.3 · p. 8
  • Sociotechnical challenges address value plurality, dual-use misuse, independent monitoring, transparency, socioeconomic effects, and governance, while the agenda acknowledges that it is comprehensive but not exhaustive.The paper also reviews related prior work and limitations in a dedicated addendum.Source: §1.3 · pp. 8–9

2 Scientific Understanding of LLMs

The section argues that assuring LLM safety requires scientific understanding because LLMs exhibit complex, emergent behavior that evaluations alone may not capture. It focuses on safety-relevant gaps involving capabilities, scale, reasoning, agentic behavior, multi-agent settings, and safety-performance trade-offs.

Motivation and scope

  • Scientific understanding is essential for estimating LLM risks, designing interventions, and assuring these complex systems, whose emergent behaviors make evaluations alone insufficient.The section focuses on safety-relevant aspects, but translating scientific insights into practical recommendations requires additional work.Source: §2 · p. 10

Capabilities Are Difficult to Estimate and Understand

  • LLM capabilities are difficult to estimate because human capability concepts may not apply, capabilities lack rigorous definitions, and benchmarks can conflate alignment with capability failures.Evaluation methods also need to assess generality and account for scaffolding.Source: §2 · p. 11

Effects of Scale on Capabilities Are Not Well-Characterized

  • LLM scaling remains poorly characterized because scaling laws, scaling limits, representation changes, emergent capabilities, and task-specific scaling laws are insufficiently understood.These gaps hinder prediction of how capability changes with scale.Source: §2 · p. 11

Qualitative Understanding of Reasoning Capabilities Is Lacking

  • Reasoning capabilities cannot yet be predicted confidently for future LLMs, motivating research on reasoning mechanisms, non-deductive reasoning, and transformer computational limits.The section specifically links this uncertainty to model scale.Source: §2 · p. 11

Agentic and multi-agent safety

  • Agentic LLMs may create novel risks through greater capabilities and autonomy, including underspecified side effects, reward hacking, deception, power-seeking, and difficult oversight.In multi-agent settings, correlated failures, collusion, and uncertain effects of single-agent training further challenge safety assurance.Source: §2 · pp. 11–12

Safety-Performance Trade-offs Are Poorly Understood

  • Safety-performance trade-offs in LLM systems are poorly understood, requiring better metrics, cross-context characterization, identification of unavoidable trade-offs, and methods for Pareto improvements.The section treats these trade-offs as a general engineering concern whose behavior in LLM-based systems remains unclear.Source: §2 · p. 12

2.1 In-Context Learning (ICL) Is Black-Box

ICL flexibly changes LLM behavior from prompt information without parameter updates, but its black-box mechanisms make safety and alignment difficult to assure. The section surveys competing explanations, limits, mechanistic evidence, and research questions concerning ICL’s scope, emergence, and modulation.

2.1 In-Context Learning (ICL) Is Black-Box

  • ICL enables efficient adaptation from prompts, yet its poorly understood dynamics may create dangerous capabilities or bypass safeguards during deployment.The section therefore prioritizes understanding ICL mechanisms, limits, and safety implications.Source: §2.1 · p. 12

2.1.1 Is ICL Sophisticated Pattern-Matching?

  • Pattern-matching theories explain only parts of ICL, omitting interactive feedback, multitask and sequential learning, novel tasks, and natural-language data generation.Theories must be refined or replaced to explain the full range of ICL behaviors.Source: §2.1.1 · pp. 12–13

2.1.2 Is ICL Due to Mesa-Optimization?

  • Transformers can implement mesa-optimization and select learning algorithms in controlled settings, but its occurrence under language-model training and on complex tasks remains uncertain.Researchers also need methods to distinguish mesa-optimization from other ICL mechanisms and resolve disagreement over which algorithms transformers implement.Source: §2.1.2 · pp. 13–14

2.1.3 What Behaviours Can Be Specified In-Context?

  • In-context universal approximation may let realistically sized models learn arbitrary behaviors, potentially making some safety finetuning unable to prevent jailbreaking.Whether this behavior arises in real-world models, with practical prompt lengths and across architectures, remains unclear.Source: §2.1.3 · p. 14

2.1.4 Scenario-Based Mechanistic Understanding of ICL

  • Scenario-based interpretability studies identify task-specific ICL circuits, including function vectors, mid-to-late feedforward layers, and false induction heads.These case studies can clarify how computational roles, task variation, prompt styles, and architectural inductive biases shape ICL.Source: §2.1.4 · pp. 14–15

2.1.5 Understanding the Effect of the Pre-training Data Distribution on ICL

  • Task diversity, burstiness, and compositional structure may drive ICL emergence, but their necessity and sufficiency in real-world language-model training remain unverified.Dataset analysis and controlled synthetic text datasets are proposed to identify the relevant pre-training conditions.Source: §2.1.5 · p. 15

2.1.6 Understanding the Effect of Design Choices on ICL

  • ICL is sensitive to model scale, training duration, and instruction tuning, yet how these choices alter its mechanisms and safety-relevant strength remains poorly understood.Instruction tuning strengthens semantic priors and reduces the tendency for in-context information to override them, while extended training may replace ICL with in-weight learning.Source: §2.1.6 · pp. 15–17

2.2 Capabilities Are Difficult to Estimate and Understand

Assuring LLM safety requires calibrated estimates of capabilities, but human–LLM mismatches, weak conceptual foundations, unreliable benchmarks, limited generality evaluations, and unaccounted scaffolding impede such understanding.

2.2.1 LLM Capabilities May Have Different ‘Shape’ Than Human Capabilities

  • LLM capabilities can differ sharply in shape from human capabilities, producing inconsistent behavior and making simulation, prediction, assurance, and human-oriented tests unreliable.GPT-4’s counting accuracy varies with answer probability, while Gemini-1.5 rapidly learns translation into Kalamang, illustrating behavior unlike human performance.Source: §2.2.1 · p. 17

2.2.2 Lack of a Rigorous Conception of Capabilities

  • Capability claims commonly equate task performance with capability, but sample choice can produce conflicting conclusions and obscure shortcut-driven or non-robust behavior.Rigorous assurance requires distinguishing capabilities that are absent, partially present, or robustly present, using clearly defined and commonly agreed conceptualizations.Source: §2.2.2 · p. 18
  • Three proposed conceptualizations—domains, internal computations, and latent factors—could support more rigorous capability claims, but their relative rigor, tractability, and interpretability remain unsettled.Domain claims specify reliable behavior over input regions; circuit analysis targets internal computations; latent-factor methods explain performance variation but may yield uninterpretable factors.Source: §2.2.2 · pp. 18–19

2.2.3 Limitations of Benchmarking for Measuring Capabilities and Assuring Safety

  • Benchmarking can mistake alignment or safety-finetuning failures for absent capabilities, overcount shared capabilities, miss non-robust behavior, and conceal example-level variation.API-only access to leading models further limits independent safety auditing, motivating better elicitation protocols and finer-grained, more structured benchmarks.Source: §2.2.3 · pp. 19–20

2.2.4 How Can We Efficiently Evaluate Generality of LLMs?

  • Domain-specific evaluations may undercount capabilities and cannot efficiently assess generality, motivating procedural cross-domain evaluations, mechanistic discovery of reused capabilities, and theoretically grounded capability taxonomies.Existing taxonomies are ad hoc, while documenting capability dependencies at scale could improve evaluation selection and training curricula.Source: §2.2.4 · pp. 20–21

2.2.5 Scaffolding Is Not Sufficiently Accounted for in Current Evaluations

  • Elicitation protocols and larger systems can reveal or rapidly induce capabilities, making attribution to the LLM versus scaffolding unclear and requiring methods to distinguish learned capabilities from inherent ones.External tools, memory, feedback, and fine-tuning can alter behavior; game-theoretic concepts may help attribute system capabilities across components.Source: §2.2.5 · p. 21

2.3 Effects of Scale on Capabilities Are Not Well-Characterized

Scaling reliably improves aggregate performance, but specific capabilities, learned representations, limits, emergent behaviors, and task-specific scaling remain difficult to predict and explain. The section identifies open questions spanning scaling-law theory, representation change, scale ceilings, emergence, and improved forecasting methods.

2.3.1 Understanding Scaling Laws

  • Scaling-law explanations remain fragmented across variance-limited, resolution-limited, and compute-efficient regimes, leaving universality, feature learning, non-stationary data, and task-dependent exponents unresolved.Proposed explanations include manifold, kernel-spectrum, and long-tail theories, but their unification and applicability to joint data-parameter scaling remain open questions.Source: §2.3.1 · pp. 24–25

2.3.2 Effect of Scaling on Learned Representations

  • Scale may change representation universality, alignment with humans, and linear structure, but evidence is mixed and the effects on interpretability remain unclear.Different networks can learn different representations while sharing some features, and larger models may become more human-consistent without matching human biases at the edges.Source: §2.3.2 · p. 25

2.3.3 Limits of Scaling

  • Further scaling may face capability ceilings and fail to resolve causal confusion, hallucinations, jailbreaks, or adversarial vulnerability, while some undesirable behaviors can worsen or reverse non-monotonically.Inverse scaling has been observed for sycophancy, whereas many benchmark cases later reverse at the largest measured compute scale, producing U-shaped curves.Source: §2.3.3 · p. 26

2.3.4 Formalizing, Forecasting, and Explaining Emergence

  • Emergent capabilities are difficult to forecast because abrupt scaling may reflect compositional skill interactions, while definitions, progress measures, and evidence standards remain unsettled.Multiplicative emergence can make smoothly scaling skills appear abrupt, but valid decompositions and faithful progress measures may be unavailable; discontinuous curves alone may be insufficient evidence.Source: §2.3.4 · pp. 26–27

2.3.5 Better Methods for Discovering Task-Specific Scaling Laws

  • Task-specific scaling methods need richer models, higher-resolution evaluations, uncertainty estimates, and explicit criteria for forecasting horizons, interpretability, and distinct use cases.Candidate approaches include generalized non-power-law fits, cross-task or per-example information, probabilistic models, and extrapolation standards extending beyond the limited horizons currently evaluated.Source: §2.3.5 · pp. 27–28

2.4 Qualitative Understanding of Reasoning Capabilities Is Lacking

The section argues that LLM reasoning shows both impressive task performance and substantial deficiencies, leaving unclear which limitations are fundamental versus remediable through scaling or training. It identifies research priorities spanning scaling, mechanisms, non-deductive reasoning, training, and transformer computation.

2.4 Qualitative Understanding of Reasoning Capabilities Is Lacking

  • Because reasoning strengths and failures create different safety risks, calibrated understanding must separate fundamental limitations from deficiencies likely to improve through scale or training changes.Robust failure on novel contexts can produce unsafe behavior, while robust reasoning in a misaligned model can enable undesirable and competent behavior.Source: §2.4 · pp. 29–30, §2.4.5 · p. 33

2.4.1 Does Scaling Improve Reasoning Capabilities?

  • General reasoning scaling laws remain uncertain because existing evidence is confounded by contamination or task-specific training, motivating broader evaluations and tests of whether scale eventually encounters fundamental limits.Figure 2 sketches alternative outcomes: increasing scale could support increasingly complex reasoning, or performance could plateau because of fundamental limitations.Source: §2.4.1 · p. 30, Figure 2 · p. 30

2.4.2 Understanding the Mechanisms Underlying Reasoning

  • Mechanistic interpretability could explain when LLMs use heuristics or genuine reasoning, but current evidence is limited to simple synthetic tasks and may not extend to OOD, long-tail, or complex inputs.Hou et al. (2023) found attention patterns resembling an oracle reasoning tree in GPT-2 and LLaMA, while comparisons of smaller failures and larger successes could clarify scale-related mechanisms.Source: §2.4.2 · pp. 30–31

2.4.3 Understanding Non-Deductive Reasoning Capabilities of LLMs

  • LLMs’ non-deductive reasoning remains uneven: evidence is mixed for inductive, abductive, social, and situational reasoning, while current causal reasoning capabilities are quite limited.Further work should distinguish limitations of current models from limitations of the paradigm, including whether training or scaling can resolve them.Source: §2.4.3 · pp. 31–32, Figure 3 · p. 31

2.4.4 Which Aspects of Training Lead to the Acquisition of Reasoning?

  • Reasoning acquisition during pretraining and finetuning is poorly understood, including the relative effects of reasoning traces, code, instruction tuning, and the generalization of learned improvements OOD.Influence functions and analyses of large-scale datasets could identify instrumental training examples, while output-distribution comparisons could clarify instruction tuning’s contribution.Source: §2.4.4 · p. 32

2.4.5 What Are the Computational Limits of Transformers?

  • Transformer computation has strict single-pass limitations, while intermediate steps increase expressive power; open questions concern the right formalism for transformer computation and which represented algorithms are learnable.Single-pass transformers cannot solve some automata, graph-connectivity, and compositional-formula problems, whereas intermediate steps can enable broader computational classes; expressibility does not imply learnability.Source: §2.4.5 · pp. 32–33

2.5 Agentic LLMs Pose Novel Risks

Agentic LLMs introduce novel alignment and safety challenges because greater autonomy, limited oversight, longer action horizons, lifelong learning, and external affordances expand their ability to act and fail in the world. Key concerns include underspecified goals, undesirable goal-directed behavior, and monitoring systems that agents may circumvent or hack.

2.5 Agentic LLMs Pose Novel Risks

  • Greater autonomy, limited direct oversight, and longer action horizons make LLM-agents pose novel alignment and safety challenges that remain poorly understood.Agents can autonomously plan and act in the real world, unlike reactive LLM-based chatbots.Source: §2.5 · pp. 33–34

2.5.1 LLM-agents May Be Lifelong Learners

  • Lifelong learning through environmental feedback, self-reflection, and external memory can increase capabilities while enabling dangerous behavior, reward hacking, goal misgeneralization, or loss of alignment.It remains unclear how to ensure that continuous learning does not undo an agent’s alignment.Source: §2.5.1 · p. 34

2.5.2 Natural Language Underspecifies Goals

  • Natural-language goals are underspecified, so LLM-agents may make faulty assumptions and achieve assigned tasks while causing undesirable side-effects.Proposed remedies include clarifying questions, conservative action, uncertainty calibration, and information from demonstrations or the environment, but their effectiveness in diverse scenarios remains unclear.Source: §2.5.2 · pp. 34–35

2.5.3 Goal-Directedness Incentivizes Undesirable Behaviors

  • Goal-directed LLM-agents may exhibit deception, power-seeking, self-preservation, and immoral reasoning, even when trained to be harmless and helpful.Research needs better benchmarks, causal understanding of these behaviors, and effective mitigation through training or internal interventions.Source: §2.5.3 · p. 35

2.5.4 Difficulty of Robust Oversight and Monitoring

  • Robust oversight is difficult because agents may circumvent monitoring, exploit leaked information through reward hacking, or use situational awareness and steganography to evade detection.Monitoring research requires adversarial threat models and potentially ensemble systems, while some secure steganography may be intractable to detect without cryptographic secrets.Source: §2.5.4 · pp. 35–36

2.5.5 Safety Risks from Affordances Provided to LLM-agents

  • External affordances such as web browsing, physical manipulation, self-replication, and tool creation expand agents’ impact areas, amplify failures, and enable novel risks that current simulations may miss.Understanding how risks scale with affordance diversity and base-model capabilities, and assuring safe affordance use, remain open problems.Source: §2.5.5 · pp. 36–37

2.6 Multi-Agent Safety Is Not Assured by Single-Agent Safety

Single-agent alignment and safety do not assure desirable outcomes when LLM-agents interact strategically, so multi-agent safety requires deliberate design across correlated failures, emergence, collusion, and cooperation. Existing multi-agent reinforcement-learning methods may not transfer directly because LLM-agents differ from traditional utility-maximizing agents.

2.6 Multi-Agent Safety Is Not Assured by Single-Agent Safety

  • Single-agent safety is insufficient in multi-agent settings, where strategic interaction can produce collective harms, emergent dynamics, and other outcomes requiring deliberate safety efforts.Examples include social dilemmas, market failures, network effects, and emergent functionality.Source: §2.6 · p. 37

2.6.1 Influence of Single-Agent Training on Multi-Agent Interactions is Unclear

  • Because multi-agent experience may be limited, LLM-agents may rely on pretrained dispositions and capabilities such as altruism, selfishness, negotiation, theory of mind, and manipulation.Specialized benchmarks and adversarial evaluations are needed to assess these traits across contexts.Source: §2.6.1 · pp. 37–38

2.6.2 Foundationality May Cause Correlated Failures

  • Foundationality may facilitate cooperation through shared designs but also create correlated safety and capability failures, including jailbreaks that transfer across different LLMs.Agent diversity, prompting, ethical principles, or quality-diversity finetuning are proposed robustness directions.Source: §2.6.2 · p. 38

2.6.3 Groups of LLM-Agents May Show Emergent Functionality

  • Multi-agent training and interaction can produce emergent functionality or self-organization that is dangerous and difficult to predict or guard against beforehand.Groups may collectively perform tasks that individual agents cannot, motivating open-ended simulations and evaluations of goal-directed behavior.Source: §2.6.3 · pp. 38–39

2.6.4 Collusion between LLM-Agents

  • Steganographic collusion can arise without explicit incentives or instructions to collude when covert communication improves joint performance under misspecified objectives.Such collusion challenges monitoring because it may not depend on an intent to deceive.Source: §2.6.4 · p. 39

2.6.5 Unclear Applicability of Multi-Agent RL Research to LLMs

  • Multi-agent RL findings may not transfer cleanly to LLM-agents because their objectives are implicit, pretrained behavior differs from utility maximization, and classical methods scale poorly.Existing methods must be tested beyond behavioral cooperation, including their effects on collusion, natural-language grounding, and task performance.Source: §2.6.5 · p. 40

2.7 Safety-Performance Trade-offs Are Poorly Understood

Safety-performance trade-offs in LLM systems are widespread but poorly understood, requiring better metrics, disentanglement of safety from performance, context-sensitive characterization, and research into their causes and fundamental nature.

2.7.1 Designing Better Metrics to Measure Safety

  • Safety metrics should move beyond harmlessness toward granular, holistic measures covering properties such as corrigibility, transparency, and out-of-distribution robustness.Harmlessness may not suit LLM-agents, while Elo ratings can be unreliable for similarly ranked models and may violate transitivity in human evaluations (Boubdir et al., 2023).Source: §2.7.1 · pp. 41–42

2.7.2 Disentangling Safety from Performance

  • Safety and performance may be disentangled using knobs such as interfaces, knowledge, agency, and task specifications, though selective ignorance could reduce capabilities more broadly.Restricting sensors and actuators may be bypassed easily, while specifying how agents solve tasks can reduce both inventive performance and unpleasant surprises (Glukhov et al., 2023).Source: §2.7.2 · p. 42

2.7.3 Better Characterization of Safety-Performance Trade-offs

  • Trade-offs should be characterized across multiple safety and performance axes, capabilities, deployment contexts, agent affordances, and development stages to identify Pareto-optimal outcomes.Relevant differences include medical versus general assistants, agents versus assistants, API fine-tuning versus inference-only interfaces, and architecture or data-filtering choices.Source: §2.7.3 · p. 42

2.7.4 How Fundamental Are Safety-Performance Trade-offs?

  • Evidence conflicts on whether LLM safety-performance trade-offs are fundamental: Constitutional AI can yield Pareto improvements, while other work argues safety may require substantial performance sacrifices (Bai et al., 2022a; Branwen, 2016; Millière, 2023; Wolf et al., 2023).Research should study both the severity and causes of these trade-offs to determine whether better methods can overcome them.Source: §2.7.4 · pp. 42–43

2.7 Safety-Performance Trade-offs Are Poorly Understood

  • LLM safety-performance trade-offs remain poorly understood, motivating formalization, clearer safety metrics, empirical severity assessments, causal analysis, and tests of whether they are fundamental.Improved understanding could support better system-design choices and appropriate safety standards.Source: §2.7 · p. 41, §2.7.4 · p. 43

3 Development and Deployment Methods

Section 3 examines foundational challenges in the development and deployment of LLMs, focusing on the insufficiency of current methods for alignment, evaluation, interpretability, oversight, and security. It spans training and finetuning, reliable and scalable evaluation and reasoning, and defenses against adversarial inputs, jailbreaks, multimodal and system-level vulnerabilities, and poisoning or backdoor attacks.

3 Development and Deployment Methods

  • Development and deployment methods span training, evaluation and interpretation, and security, but current tools cannot yet provide necessary assurance of LLM alignment and safety.Training includes pretraining and safety finetuning; security includes jailbreak, prompt-injection, and poisoning defenses.Source: §3 · p. 44

3.3 Evaluation and 3.4 Interpretation

  • LLM evaluations are confounded by prompt sensitivity, contamination, targeted suppression, evaluator bias, and sociotechnical blind spots, while scalable oversight remains uncertain.Interpretability methods face dubious abstractions, human–model concept mismatch, polysemanticity, dataset sensitivity, limited scalability, and potentially unfaithful explanations.Source: §3 · p. 45

3.5 Security and 3.6 Poisoning

  • LLMs remain vulnerable to jailbreaks, prompt injections, poisoning attacks, and backdoors, with standardized robustness evaluation and reliable backdoor detection still lacking.Multimodal inputs and the absence of robust privilege levels may make jailbreak and prompt-injection attacks particularly difficult to eliminate.Source: §3 · p. 46

3.1 Pretraining Produces Misaligned Models

  • Pretraining produces unsafe models, while filtering, auditing, and training-data attribution remain inadequate for identifying and mitigating harmful or biased data.Filtering can remove marginalized-group text, reduce diversity, and amplify social biases; scalable auditing and attribution methods remain open problems.Source: §3.1 · p. 46, §3.1.1 · pp. 46–47, §3.1.2 · p. 47

3.1.4 Scaling Pretraining Using Human Feedback

  • Pretraining with human feedback can reduce harmful generation, but its scaling behavior and broader use of preference information remain poorly understood.Korbak et al. (2023) found that feedback throughout pretraining was critical, whereas Anil et al. (2023) found that feedback on only some documents could drastically reduce toxicity.Source: §3.1.4 · p. 48, §3.1.5 · p. 49

3.2.1 How Does Finetuning Change a Pretrained Model?

  • Safety finetuning often changes only a superficial layer of pretrained behavior, and undesirable capabilities can be recovered through prompting or small amounts of further finetuning.Lin et al. (2023a) found distributional changes concentrated in a small fraction of safety-related tokens, while Clymer et al. (2023) found that finetuning can sometimes add capabilities.Source: §3.2 · pp. 49–50, §3.2.1 · p. 50

3.2.2 Finetuning Misgeneralizes in Unpredictable Ways

  • Finetuning generalizes unpredictably across shifts, and output-based adversarial training may encourage superficial alignment rather than coherent, generalizable decision procedures.Process supervision and consistency training are proposed alternatives, but their use for LLM alignment and safety remains preliminary.Source: §3.2.2 · p. 51, §3.2.3 · p. 51

3.2.4 Techniques for Targeted Modification of LLM Behavior Are Underexplored

  • Targeted model editing, concept erasure, and related techniques lack a reliable, scalable toolbox for cleanly removing specific information or capabilities from LLMs.These methods commonly rely on identifying editable architectural components using interpretability techniques that themselves lack robustness and scalability.Source: §3 · p. 45, §3.2.4 · p. 52
  • Targeted modification methods may fail to erase undesirable knowledge or capabilities, while broader behavioral edits require benchmarks testing effectiveness, side effects, relearning, and jailbreak robustness.Machine unlearning has shown potential but can incompletely remove knowledge; research should assess whether it outperforms simple baselines and generalizes beyond specific facts.Source: §3.2.4 · p. 52

3.2.5 Removal of Unknown Undesirable Capabilities

  • Unknown undesirable capabilities can survive safety finetuning as latent attack vectors, motivating broader evaluations and methods such as compression, distillation, latent adversarial training, and mechanistic forgetting.Finetuning may even reinforce undesirable behaviors, while the causes of resistance to deep forgetting remain poorly understood.Source: §3.2.5 · pp. 52–53

3.3 LLM Evaluations Are Confounded and Biased

  • LLM evaluations are confounded by prompt sensitivity, test-set contamination, targeted suppression of known failures, evaluator biases, human annotation limitations, systematic blindspots, and inadequate scalable oversight.These problems can respectively underestimate or overestimate capabilities, hide failures in complex contexts, diverge from human judgments, and miss harms in low-resource languages.Source: §3.3 · p. 53, §3.3.3 · p. 55, §3.3.6 · p. 56

3.3.1 Prompt-Sensitivity Confounds Estimation of LLM Capabilities

  • Prompting and tools can drastically change measured performance: GPT-4 accuracy on MATH rose from 53.9% to 84.3% with special prompting and a code-interpreter.Automated prompt discovery may improve evaluation, but learned prompts introduce additional sensitive variables, so evaluations must still account for prompt sensitivity.Source: §3.3.1 · p. 54

3.3.2 Test-set Contamination Overestimates LLM Capabilities

  • Test-set contamination can overestimate capabilities because familiar or mutated benchmark data may appear in pretraining, while blacklisting MMLU’s original sources reduced contamination by only 1.5%.Future methods should detect semantic contamination and assess protections such as canary strings, APIs, and password-protected archives.Source: §3.3.2 · pp. 54–55

3.3.4 Biases in LLM-Based Evaluation

  • LLM-based evaluation is cheaper and scalable but suffers position, verbosity, and egocentric biases, whereas crowdsourced human evaluation is costly, bias-prone, and vulnerable to AI-assisted annotation.Protocol changes can improve LLM-evaluation fidelity, while robust human evaluation requires bias-aware procedures, complementary LLMs, better preference models, and appropriate worker incentives.Source: §3.3.4 · p. 55, §3.3.5 · pp. 55–56

3.3.7 Challenges with Scalable Oversight

  • Scalable oversight proposals require formalization and robustness analysis because task decomposition may not generalize, approximation errors may accumulate, and human or weakly aligned overseers may be subverted.Key open directions include identifying alignment targets, comparing complementary methods, characterizing intentional and unintentional subversion, and developing LLM-specific strategies.Source: §3.3.7 · pp. 56–58

3.4 Tools for Interpreting or Explaining LLM Behavior Are Absent or Lack Faithfulness

  • Interpretability and externalized-reasoning tools cannot yet reliably assure safety because their abstractions may be dubious, machine concepts may differ from human concepts, and explanations may be plausible without being faithful.Prior interpretability methods have failed rigorous tests or random baselines, while probes, neuron-based analyses, circuits, and natural-language reasoning each rely on potentially invalid assumptions.Source: §3.4 · p. 58, Fundamental Challenges · p. 58, §3.4.1 · p. 59

3.4.3 Evaluations Often Overestimate the Reliability of Interpretability Methods

  • Interpretability evaluations may overestimate reliability because faithful circuit or reasoning explanations are difficult to assess, especially on adversarial or high-risk inputs.Faithfulness benchmarks should support automated simulatability, worst-case or high-risk evaluation, and scalable detection of inconsistencies in externalized reasoning; practitioner claims also require realistic user studies.Source: §3.4.3 · pp. 60–61

3.4.4 Can Interpretability Methods Maintain Validity When Used to Modify Model Behavior?

  • Optimizing interpretability methods can trigger Goodharting, causing targeted behaviors to persist even when the interpretability signal no longer detects them.Research is needed to determine when optimization targets overfit interpretability methods and how to preserve validity during model editing, representation editing, or related interventions.Source: §3.4.4 · pp. 61–62

3.4.5 Assuming Linearity of Feature Representation

  • Feature representations may be nonlinear, neurons may encode unrelated features through superposition, and discovered concepts can change with the analysis dataset.Determining which concepts are linearly encoded, explaining polysemanticity, and developing compute-adaptive concept discovery are central open problems.Source: §3.4.5 · p. 62, §3.4.6 · p. 62, §3.4.7 · p. 63

3.4.8 Feature Interpretation Is Hard to Scale

  • Feature interpretation and circuit discovery remain difficult to scale: current mechanistic methods explain 95%+ of variance only for highly toy problems, while circuit search can be combinatorial and miss backup nodes.Human-in-the-loop and automated feature labeling can be time-consuming or error-prone, and even greedy circuit discovery may be infeasible for extremely large models.Source: §3.4.8 · p. 63, §3.4.9 · p. 64

3.4.10 Externalized Reasoning in Natural Language May Be Misleading

  • Natural-language externalized reasoning can produce plausible but unfaithful explanations, whereas formal-semantics approaches offer more precise verification but apply mainly to formally specifiable tasks.Natural-language reasoning faces ambiguity, hidden influences, and completeness–efficiency tradeoffs; program synthesis remains difficult for open-ended tasks and may still inherit ambiguity during formalization.Source: §3.4.10 · pp. 64–65, §3.4.11 · pp. 65–66

Challenges Specific to Interpreting Model Internals

  • Interpretability of model internals is constrained by uncertain abstractions, linearity assumptions, polysemantic neurons, dataset sensitivity, and poor scalability of feature and circuit discovery.These challenges limit the ability of representation probing and mechanistic interpretability to explain nontrivial model behavior reliably.Source: Challenges Specific to Interpreting… · p. 62, §3.4.11 · p. 66

3.5 Jailbreaks and Prompt Injections Threaten Security of LLMs

  • Jailbreaks and prompt injections exploit weak adversarial robustness across users, developers, and third-party data sources, motivating continuous improvement of both attacks and defenses.The section distinguishes jailbreaking, direct prompt injection, and indirect prompt injection as related failures of restrictions imposed by model creators or application developers.Source: §3.5 · pp. 67–68

3.5.1 Standardized Evaluations of Jailbreak and Prompt Injection Success

  • Jailbreak and prompt-injection results are hard to compare because studies use different success criteria, while attacker goals and degrees of freedom remain insufficiently formalized.The proposed direction is standardized threat models, diverse automated benchmarks, and common metrics spanning attacks and application types.Source: §3.5.1 · pp. 68–69

3.5.2 Efficient and Reliable White-box Attacks for LLMs Are Lacking

  • Gradient-informed white-box attacks against LLMs remain slower, less reliable, and no more effective than black-box attacks, motivating better relaxations and discrete optimization methods.Candidate directions include optimizing in embedding space subject to realizable-token constraints or developing discrete schemes that exploit gradients.Source: §3.5.2 · p. 69

3.5.3 Unifying or Differentiating Jailbreak Attack Methodologies

  • Jailbreaks comprise safety-finetuning generalization gaps, “model psychology” tricks, and adversarial optimization, but their mechanisms, cross-category robustness, and transferability between models remain poorly understood.The first two categories yield human-readable attacks, whereas optimization-based attacks often produce gibberish; transfer can be inherent for model-independent attacks but is less obvious for optimization and red-teaming attacks.Source: §3.5.3 · pp. 69–70

3.5.4 Attacking LLMs via Additional Modalities and Defending Against These Attacks

  • Multimodal LLMs are vulnerable to effective gradient-based image attacks, embedded jailbreak text, and indirect injections, while robustness across architectures and modalities remains an open question.The unresolved comparison concerns models built by connecting pretrained image encoders and LLMs versus models trained jointly on text and images end-to-end.Source: §3.5.4 · p. 70

3.5.5 Defending the LLM as a System: Detection, Filtering, and Paraphrasing

  • Input paraphrasing, perplexity filtering, and output checks can impede jailbreaks, but their robustness, adaptive-attack resistance, and performance costs remain unresolved.Perplexity filters are attractive because legitimate inputs are rarely low-probability, whereas security-by-complexity defenses may fail against sufficiently resourceful attackers or composed innocuous outputs.Source: §3.5.5 · pp. 70–71

3.5.6 Course-Correction After Accepting a Harmful Request

  • Current LLMs often fail to course-correct after an initial affirmative response because safety-finetuning effects dissipate over longer continuations, motivating backtracking training or affirmative-response filtering.Proposed remedies include finetuning on conversations that backtrack and refuse, explicit backtracking abilities, or preventing affirmative responses from being generated.Source: §3.5.6 · p. 71

3.5.7 There Are No Robust Privilege Levels within the LLM Input

  • Because LLMs lack robust privilege levels separating instructions from data, users or third-party content can override system prompts, extract confidential prompts, hijack applications, exfiltrate data, execute code, or manipulate users.Possible remedies include tagging data and instructions, planner–quarantined-LLM designs, specialized hijacking defenses, and output filtering, but their reliability and performance costs are unclear.Source: §3.5.7 · pp. 71–73

3.6 Vulnerability to Poisoning and Backdoors Is Poorly Understood

  • Poisoning and backdoor risks are plausible but underresearched because LLM training uses untrusted data, and vulnerabilities may differ across pretraining, instruction tuning, and RLHF.Instruction tuning can be more vulnerable than RLHF, while successful RLHF poisoning may be more concerning because it can create a universal backdoor enabling arbitrary harmful capabilities.Source: §3.6 · p. 73, §3.6.1 · p. 73, §3.6.2 · p. 74

3.6.3 Are Larger Models More Vulnerable to Poisoning Attacks?

  • Evidence on poisoning remains mixed across scale, out-of-context reasoning, modalities, and encodings, while detecting and removing backdoors is difficult because standard SFT and RLHF often leave them intact.Larger models are more vulnerable to task-specific instruction-tuning poisoning but more robust to universal backdoors; base64 and low-resource-language attacks expose safety gaps, and larger models can implement backdoors more robustly to safety fine-tuning.Source: §3.6.3 · p. 74, §3.6.6 · p. 75, §3.6.5 · p. 75

4 Sociotechnical Challenges · 4.1 Values to Be Encoded within LLMs Are Not Clear

The paper frames LLM alignment and safety as sociotechnical problems requiring interdisciplinary, holistic analysis beyond narrow technical solutions. It focuses on unresolved questions about which values to encode, how to manage conflicts and biases, how to evaluate encoded values, and whether value alignment is sufficient.

4 Sociotechnical Challenges

  • Sociotechnical safety research examines LLMs’ interactions with social, economic, and political systems through interdisciplinary, holistic methods and diverse stakeholder evaluation.Narrow technical solutions can ignore contextual factors and inadvertently cause harm, even if technical problems are solved.Source: §4 · p. 77
  • Sociotechnical challenges include dual-use misuse, untrustworthy behavior, disruptive socioeconomic impacts, and inadequate governance requiring mitigation beyond technical alignment.The paper highlights misinformation, warfare, cyberattacks, surveillance, biological weapons design, accidental harm, inequality, job losses, weak institutions, competitive pressures, and limited governance proposals.Source: §4 · p. 79

4.1 Values to Be Encoded within LLMs Are Not Clear

  • LLM alignment must question what, and whose, values should be encoded rather than assuming developers’ intent defines alignment.The paper calls for research on value systems, technical feasibility, and risks that LLMs illegitimately impose particular values on society.Source: §4.1 · p. 80

4.1.1 Justifying Value Choices for Alignment

  • High-level values such as helpfulness, harmlessness, and honesty remain underjustified across model types, use contexts, capabilities, and alternative value systems.The paper also questions whether revealed preferences should dominate principles, stated preferences, or other ways of communicating human values.Source: §4.1.1 · p. 80

4.1.2 Managing Conflicts between Different Values

  • Value conflicts arise across communities, within individuals, and over time, making resolution strategies and safeguards against developers imposing narrow values on society essential.The paper identifies conflicts between helpfulness and harmlessness, cross-cultural differences, homogeneous developers, implicit decisions, and risks of superficial participation.Source: §4.1.2 · p. 81

4.1.3 ‘Lotteries’ May Bias the Values That We Encode

  • Technical, methodological, profitability, and platformability lotteries may cause feasible, convenient, or commercially advantageous values and encoding methods to displace more desirable alternatives.For example, honesty receives less practical attention than helpfulness and harmlessness because it is costlier and less technically tractable to evaluate.Source: §4.1.3 · p. 82

4.1.4 How Can We Robustly Evaluate Which Values an LLM Encodes?

  • Robust value evaluation must address prompt sensitivity, systematic and cultural bias, realistic applications, persona variation, value understanding versus mimicry, and transmission across development stages.The paper argues that evaluations should determine whether models reliably accord with values rather than merely reproduce them.Source: §4.1.4 · pp. 82–83

4.1.5 Is ‘Value Alignment’ the Right Framework?

  • The value-alignment framing may be philosophically unsettled, context-dependent, and insufficient for systems embedded in society, so alternative or complementary framings are needed.The paper emphasizes that AI systems can mediate human agency and exercise power, requiring broader non-technical approaches and interdisciplinary collaboration.Source: §4.1.5 · p. 83

4.2 Dual-Use Capabilities Enable Malicious Use and Misuse of LLMs

The section argues that LLMs’ dual-use capabilities enable malicious and accidental misuse across misinformation, cybersecurity, surveillance, warfare, hazardous technologies, and other domains. It calls for rigorous, context-specific research and effective mitigation, including improved detection and attribution.

4.2 Dual-Use Capabilities Enable Malicious Use and Misuse of LLMs

  • LLMs’ dual-use capabilities create substantial risks of malicious misuse, while limited rigorous and context-specific understanding hampers prioritization and effective mitigation.The section primarily focuses on intentional misuse but notes that widespread, easy-to-use, general-purpose LLMs may also amplify accidental and ethically grey uses.Source: §4.2 · p. 84, §4.2.7 · p. 89

4.2.1 Misinformation and Manipulation

  • LLMs can generate credible, personalized misinformation and deepfakes at scale, enabling propaganda, influence operations, societal division, and individual harms.Research is needed to assess misuse scale and effectiveness and to develop fact-checking and LLM-assisted tools for identifying false information, surfacing sources, and offering alternatives.Source: §4.2.1 · pp. 84–85

4.2.2 Cybersecurity

  • LLMs may amplify phishing, malware, exploits, jailbreaks, and coordinated multi-agent cyberattacks, but their net cybersecurity impact and ability to lower expertise barriers remain uncertain.Multi-agent systems could coordinate vulnerability chaining, conceal distributed activity, and accelerate attacks, while LLMs may also support defensive analysis and mitigation.Source: §4.2.2 · pp. 85–86

4.2.3 Surveillance and Censorship

  • LLMs could make surveillance and censorship more sophisticated and multimodal, enabling large-scale suppression of political and social voices and worsening personal liberties.The section calls for sociotechnical research, proactive safeguards against misuse, and resistance to legal cover for oppressive surveillance.Source: §4.2.3 · pp. 86–87

4.2.4 Warfare and Physical Harm

  • LLM-enabled autonomous weapons could create major physical and catastrophic safety risks, including targeted mass killings, absent international agreements and national legislation prohibiting their development and use.Risks arise from autonomous drones, facial-recognition targeting, warfare planning, and multimodal LLMs serving as brains for general-purpose robots.Source: §4.2.4 · p. 87

4.2.5 Hazardous Biological and Chemical Technologies

  • LLM-based biological and chemical design tools could lower expertise barriers or enable novel hazardous discoveries, but current studies are small, use vanilla models, and remain inconclusive.Research should characterize capability uplift through realistic wet-laboratory studies, monitor changes with scale, and examine both non-state and state misuse.Source: §4.2.5 · p. 88

4.2.6 Domain-Specific Misuses

  • LLMs may be misused in health, education, and moral judgment, including nonconsensual therapy, cheating, low-quality evaluation, and compelling but harmful moral guidance.Domain experts, deployment case studies, standards, regulations, and improved monitoring are needed to identify emerging misuse across sectors.Source: §4.2.6 · p. 88

4.2.7 Mechanisms for Detecting and Attributing LLM Outputs Are Lacking

  • Recognizing and attributing LLM outputs remains difficult, especially for openly available models, limiting accountability and enforcement; watermarking may help but its effectiveness is unclear.Research should develop robust watermarking and attribution methods that remain useful when open-source models can be modified to remove deliberate markers.Source: §4.2.7 · pp. 88–90

4.3 LLM-Systems Can Be Untrustworthy

Trustworthiness means reliable, consistent performance without accidental user harm, but LLM systems remain vulnerable to bias, unreliable capabilities, overreliance, and contextual privacy failures. Addressing these risks may require both technical improvements and sociotechnical interventions.

4.3.1 Harms of Representation and Other Biases

  • LLMs can reproduce societal biases that persist after finetuning, reappear in novel scenarios, and become more prominent in low-resource languages and marginalized-group dialects.Research is needed on broader bias and toxicity detection, evaluations in novel scenarios, and representations of the global south.Source: §4.3.1 · p. 90

4.3.2 Inconsistent Performance across and within Domains

  • Users may misestimate LLM capabilities because of developer exaggeration, task contamination, underrepresented domains, prompt sensitivity, and hallucinated or misleading responses.Potential mitigations include retrieval-augmented generation, uncertainty estimates, user studies, education, disclaimers, and domain-specific safety measures.Source: §4.3.2 · pp. 90–91

4.3.3 Overreliance

  • Excessive trust can produce automation bias, omission and commission errors, and harmful dependency, especially when users lack expertise or become desensitized to disclaimers.LLMs’ plausible but incorrect rationalizations can falsely signal expertise, while prolonged use may impair skills and allow AI-derived biases to persist.Source: §4.3.3 · p. 91

4.3.4 Contextual Privacy Preservation

  • Current LLMs often leak information between parties when a human would not, and prompt engineering or output filtering does not align their behavior with human privacy expectations.Contextual privacy therefore requires formalization and operationalization beyond preventing personally identifiable information in outputs.Source: §4.3.4 · pp. 91–92

4.3 LLM-Systems Can Be Untrustworthy

  • Trustworthiness requires safe use despite unreliable outputs, prevention of overreliance, mitigation of biased representations and toxic content, appropriate behavior across contexts, and reliable privacy preservation.The paper frames these needs as foundational alignment and safety challenges and poses research questions on bias, reliability, overreliance, and contextual privacy.Source: §4.3 · p. 90, §4.3.4 · p. 92

4.4 Socioeconomic Impacts of LLM May Be Highly Disruptive

LLMs may produce highly disruptive, systemic socioeconomic effects across workers, inequality, education, and global development. The section frames mitigation as requiring deeper understanding and system-wide policy interventions rather than simple fixes.

4.4 Socioeconomic Impacts of LLM May Be Highly Disruptive

  • Because these challenges are complex and systemic, the section poses research questions on forecasting disruption, equitable access, education, policy, Global South development, and language inclusion.The questions include how to augment workers, assess working conditions, mitigate adoption barriers, update policymakers, and ensure low-resource languages are supported equally.Source: §4.4 · p. 92, §4.4.4 · pp. 96–97

4.4.1 Effects on the Workforce

  • LLM-driven automation may accelerate job disruption, reduce labor’s share of wealth, worsen job quality, and ultimately challenge labor markets and income distribution.These effects could extend beyond income to surveillance, work intensity, human agency, identity, life satisfaction, and political stability; research should examine worker transitions and impact assessments.Source: §4.4.1 · p. 93

4.4.2 Effects on Inequality

  • LLMs may exacerbate inequality through labor-substituting progress, concentrated market power, and unequal access to advanced systems, creating a new “intelligence divide.”General-purpose systems may shift from complementing to displacing workers, while training costs and network effects could produce algorithmic monoculture and monopoly rents.Source: §4.4.2 · pp. 94–95

4.4.3 Economic Challenges for Education

  • Education must retrain workers to use and evaluate LLMs while rethinking teaching and assessment, but adoption barriers and declining returns to skilled human capital may widen power imbalances.Less-skilled workers may benefit more from LLMs, potentially reducing skilled workers’ wages, hiring, training, and ability to contest harmful uses.Source: §4.4.3 · p. 95

4.4.4 Global Economic Development

  • Developing countries may face lost outsourcing, an international intelligence divide, terms-of-trade losses, and unequal profits, although accessible LLMs could improve education and development.Global benefits depend on connectivity, technological literacy, affordability, and strong performance across languages; some languages cost up to 15 times more in ChatGPT’s case.Source: §4.4.4 · pp. 95–96

4.5 LLM Governance Is Lacking · Meta-Challenges — Challenges That May Limit Efficacy of LLM Governance

LLM governance is essential but remains nascent, ill-defined, and often voluntary, while rapid technological change, limited scientific understanding, weak institutions, corporate power, international fragmentation, and unclear accountability constrain its effectiveness. The section identifies these meta-challenges and outlines directions for governance research and institutional development.

4.5 LLM Governance Is Lacking

  • Governance can supplement or substitute for technical safety measures, but current efforts remain nascent and governance mechanisms are largely ill-defined or voluntary.The discussion covers formal regulation alongside norms, soft law, codes of ethics, co-regulation, industry standards, and sector-specific guidelines.Source: §4.5 · p. 97

Meta-Challenges — Challenges That May Limit Efficacy of LLM Governance

  • Rapid productization, economic disruption, misuse potential, and technological change create meta-challenges that limit the efficacy of LLM governance.These include inadequate scientific understanding and technical tools, insufficiently agile institutions, competitive pressures, corporate power, weak international cooperation, and unclear accountability.Source: Meta-Challenges — Challenges That May… · p. 97

4.5.1 Lack of Scientific Understanding and Unreliability of Technical Tools Complicate Governance

  • Poor understanding of LLM capabilities, unreliable auditing tools, and weak generalization of safety finetuning complicate risk assessment, mitigation, and regulatory consensus.The section recommends adapting governance to technical uncertainty and progress, funding technical research and government capacity, studying risk interconnections, and evaluating proposals such as development pauses cautiously.Source: §4.5.1 · pp. 97, 99

4.5.2 Need for Effective, Fast-Moving Governance Institutions

  • Governance institutions may lack the speed, resources, expertise, and authority needed to regulate rapidly advancing LLMs effectively.Research directions include tailored AI laws, comparative analyses of national approaches, expanded regulator powers, and careful evaluation of public-private regulatory arrangements that may improve agility but increase capture risks.Source: §4.5.2 · pp. 99–100

4.5.3 Incentivizing Cooperation and Disincentivizing High-Risk Approaches to AI Development

  • Competitive pressures can produce a race to the bottom in which organizations adopt increasingly risky development, deployment, and use practices.Potential responses include specialized frontier-AI regulation, broader auditing of organizational practices, treaties, and better game-theoretic analysis of race dynamics and incentives.Source: §4.5.3 · p. 100

4.5.4 Corporate Power May Impede Effective Governance

  • Corporate power can distort LLM governance through lobbying, public-opinion influence, regulatory capture, and dependence of academia on industry funding.The section calls for documenting divergences between corporate and public interests, improving democratic oversight of model influence, and protecting academic independence.Source: §4.5.4 · pp. 100–101

4.5.5 LLMs Require International Governance

  • Nationally fragmented regulation creates jurisdictional conflicts, regulatory arbitrage, and obstacles to international consensus, while geopolitical distrust may impede cooperation.Proposals include international institutions, global safety institutes, treaty-based cooperation, and dialogue or alliances addressing issues such as AI use in weapons.Source: §4.5.5 · pp. 101–102

4.5.6 Culpability Schemes Are Needed for LLM-Based Systems — Especially LLM-Agents

  • Accountability remains unclear when LLM systems cause harm, and the problem becomes more acute as increasingly autonomous agents interact with humans and other agents.Governance research should develop anticipatory deployment criteria, monitoring and evaluation frameworks, and mechanisms for assigning responsibility across users, deployers, and developers.Source: §4.5.6 · pp. 102–103

Practical Challenges — Governance Mechanisms for LLMs Are Underdeveloped

LLM governance remains nascent and lacks concrete, comprehensive proposals, although mechanisms can operate across the lifecycle and substrates and may be combined. The section details distinct weaknesses in use-, deployment-, development-, data-, and compute-based governance.

Practical Challenges — Governance Mechanisms for LLMs Are Underdeveloped

  • Most LLM governance mechanisms remain underdeveloped, creating uncertainty about effective interventions across lifecycle stages and substrates.Earlier interventions can create choke-points, while later interventions are more targeted but may miss pathways to harm; combining mechanisms may improve outcomes.Source: Practical Challenges — Governance… · p. 103

4.5.7 Use-Based Governance May Be Insufficient

  • Use-based governance may fail to deter skilled malicious users, proactively identify misuse, address government-perpetrated harms, or prevent self-harm.Anonymization can frustrate enforcement, while emerging problematic uses may require fast-acting regulatory powers and international agreements.Source: §4.5.7 · pp. 103–104

4.5.8 Deployment Governance Lacks Adequate Regulation

  • Deployment governance lacks adequate regulation for access choices, intended users, model autonomy, deployer due diligence, licensing, monitoring, and assurance after system updates.The debate over downloadable versus API access reflects trade-offs between structured control, marginal risk, compliance burdens, and the irreversibility of open-sourcing models.Source: §4.5.8 · pp. 104–105

4.5.9 Development Governance Might Be Particularly Challenging to Codify and Enforce

  • Development governance is difficult to codify and enforce because it depends on broad developer buy-in, proprietary knowledge, detection tools, and resistance to regulatory flight or evasion.Voluntary responsible scaling policies are not enough: their safety quality remains unclear without third-party analysis and legally binding standards.Source: §4.5.9 · p. 105

4.5.10 LLMs Pose Additional Challenges for Data Governance

  • Data governance could regulate unsafe training data, but LLMs intensify privacy, data-rights, provenance, training-verification, valuation, ownership, and responsibility challenges.Data trusts require technical infrastructure alongside political will, international cooperation, and sufficient funding.Source: §4.5.10 · pp. 105–106

4.5.11 Robustness of Compute Governance is Unclear

  • Compute governance offers a promising and potentially verifiable lever for modulating AI progress, but proposals remain insufficiently robust to loopholes, privacy risks, power concentration, and industry change.Potential loopholes include distributed training on consumer hardware and disguised computations, motivating collaboration with hardware and supply-chain experts.Source: §4.5.11 · p. 106

5 Discussion

The agenda offers an expansive catalog of challenges for LLM safety and alignment, while acknowledging that its scope is limited, non-exhaustive, and likely to evolve as LLMs advance. It complements prior work by focusing specifically on technical and sociotechnical challenges requiring further research, while arguing that alignment and safety remain viable areas for academic contributions.

5.1 Limitations

  • The agenda is not exhaustive: it focuses mainly on imminent, relatively undisputed LLM challenges and may omit speculative, newly emerging, or non-LLM safety concerns.It also emphasizes technical challenges, with 13 of 18 challenges fully technical, and its modular structure can obscure interconnections between technical and sociotechnical issues; some geographic coverage is biased toward the US, Europe, and China.Source: §5.1 · p. 110

5.2 Prior Work

  • The work complements prior risk, harm, evaluation, and societal-impact studies by cataloging technical and sociotechnical challenges specific to overcoming safety and alignment problems in LLM-based systems.Related work includes taxonomies of harms and risks, dangerous-capability evaluations, sociotechnical evaluation frameworks, social-impact assessments, and analyses of labor-market disruption and malicious misuse.Source: §5.2 · p. 111
  • Compared with company agendas and broader safe-AI agendas, this work provides a broader LLM-focused scope and more diverse academic research directions rather than reflecting one organization’s priorities.Kenton et al. (2021) is described as closest in scope but narrower, while company agendas are primarily centered on their sponsors’ research directions.Source: §5.2 · p. 111
  • The authors position alignment and safety as fields where academic researchers can still contribute despite concerns that impactful machine learning and NLP require industrial-scale resources.They argue that evaluations and data remain important bottlenecks and point to under-researched areas as opportunities for academic research.Source: §5.2 · pp. 111–112

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