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A Hazard Analysis Framework for Code Synthesis Large Language Models

Heidy Khlaaf, Pamela Mishkin, Joshua Achiam, Gretchen Krueger, Miles Brundage

arXiv:2207.14157v1cs.SEcs.AI

TL;DR

Code-generating models may be useful, but their limitations and misuse potential pose safety challenges. The paper combines capability evaluation with hazard analysis for Codex-like systems; the evaluation provides a capability baseline and reports differing results across task types.

  • Problem

    Code-generating models can be useful, but their limitations, alignment problems, misuse potential, and possible contribution to destabilizing technical progress pose safety challenges.

    Source: §1 · p. 1

  • Method

    The paper pairs capability evaluation of complex, higher-level specifications with hazard analysis for Codex-like models deployed in user-facing systems such as APIs.

    Source: §1 · p. 2, §2.1 · p. 3, §3 · p. 9

  • Results

    The evaluation provides a generative-capabilities baseline, finding poor performance on concurrency synthesis but productive boilerplate generation for complex machine-learning tasks.

    Source: §2.3.1 · pp. 7–8

  • Takeaways & Limitations

    The capability baseline informs Codex evaluation and risk assessment because code-synthesis LLM capacities, limitations, and failure modes are not yet fully understood.

    Source: §2.3.1 · p. 8

    Codex's proficiency against the evaluation criteria is difficult to state with absolute certainty, and its training was primarily in Python, JavaScript, TypeScript, and Ruby.

    Source: §2.3.1 · p. 7

Abstract

from arXiv · show

Codex, a large language model (LLM) trained on a variety of codebases, exceeds the previous state of the art in its capacity to synthesize and generate code. Although Codex provides a plethora of benefits, models that may generate code on such scale have significant limitations, alignment problems, the potential to be misused, and the possibility to increase the rate of progress in technical fields that may themselves have destabilizing impacts or have misuse potential. Yet such safety impacts are not yet known or remain to be explored. In this paper, we outline a hazard analysis framework constructed at OpenAI to uncover hazards or safety risks that the deployment of models like Codex may impose technically, socially, politically, and economically. The analysis is informed by a novel evaluation framework that determines the capacity of advanced code generation techniques against the complexity and expressivity of specification prompts, and their capability to understand and execute them relative to human ability.

1 INTRODUCTION

Code-generating models may be useful, but their limitations and potential misuse make their safety implications a concern. The introduction presents a capabilities evaluation to inform a hazard analysis for Codex-like systems, demonstrated through an API serving Codex outputs.

1 INTRODUCTION

  • Prior code-generation evaluations emphasized simple function- or module-level tasks and did not consider safety implications such as fairness, bias, or discrimination.Source: §1 · p. 2
  • The hazard analysis identifies risk factors that could cause novel harms, intended as foundations for safety efforts for general-purpose LLMs.The approach is demonstrated for an API that serves Codex outputs to end users.Source: §1 · p. 2
  • Because model hazards and failure modes are poorly understood, the authors make methodical capability evaluation a starting point; interpreting and classifying outputs requires significant human-expert effort.Source: §1 · p. 2
  • The capability evaluation adapts specification complexity and expressivity attributes to natural-language prompts, benchmarks them, and assesses Codex on increasingly complex, higher-level specifications.The evaluation is intended to measure code-generation capabilities.Source: §1 · p. 2

2 EVALUATION OF CAPABILITIES OF LANGUAGE MODEL-BASED CODE GENERATION

This section assesses code-generation capability by considering specification complexity and expressivity, whether models understand and execute prompts relative to human ability, and qualitative attributes of specifications. It describes Codex as performing better on familiar, constrained tasks than on unique, complex, or higher-level specifications, with capability also depending on prompt wording and training-data coverage and outputs not guaranteed to be correct.

2 EVALUATION OF CAPABILITIES OF LANGUAGE MODEL-BASED CODE GENERATION

  • Existing code metrics focus on output correctness or complexity, so the framework also evaluates specification prompts and models’ ability to understand and execute them relative to human ability.Cyclomatic complexity is associated with defect likelihood, while algorithmic complexity measures runtime; neither directly assesses human-level capability.Source: §2 · p. 3

2.1 Specification Complexity and Expressivity

  • Natural-language requirements are expressive but imprecise, while formal specifications are impractical for everyday development and verification of Codex’s target languages, motivating adapted prompt-complexity attributes.The paper adapts attributes of formal specifications to natural-language prompts, including reasoning about computations and states at different abstraction levels.Source: §2.1 · pp. 3–4

2.2 Specification Abstractions

  • Higher-level requirements express system intent independent of implementation, while lower-level requirements add design detail and reduce the gap between specifications and code.The model must infer lower-level specifications from higher-level prompts, which the paper describes as more ambiguous and difficult.Source: §2.2 · pp. 4–5

2.3 Computational and State Reasoning

  • The proposed reasoning attributes cover variable interdependencies, temporal properties, concurrency, nondeterminism, and hyperproperties such as information-flow policies.Temporal reasoning includes safety and liveness; concurrency attributes include fairness, synchronization, and freedom from race conditions.Source: §2.3 · pp. 5–6
  • The framework also treats specification-independent coding practices as necessary attributes, including reusable code, architecture selection, varied programming constructs, and wide applicability across domains.Wide applicability means producing satisfactory solutions across domains such as embedded systems, web applications, and console applications.Source: §2.3 · p. 6

2.3.1 Evaluation and Limitations.

  • A consequential word can change correctness, and prompt factors and training-data coverage affect performance; Codex’s training in Python, JavaScript, TypeScript, and Ruby may limit domain-specific synthesis.The authors caution that these influences make it difficult to state with absolute certainty whether Codex meets the evaluation criteria.Source: §2.3.1 · p. 7
  • Codex handles relationships among two or three variables but struggles to infer intended outputs when reasoning across four or more, especially for unique prompts.The reported difficulty persisted even with relatively short, not especially high-level specifications; the authors expect it to continue unless such specifications occur frequently in training data.Source: §2.3.1 · p. 7
  • Codex performs relatively well on short, narrow safety, liveness, or termination prompts, but fails on more complex unique specifications; common exercises can still produce intended results.The failures also occurred for prompts that were not particularly high-level and that specified design or programming constructs.Source: §2.3.1 · p. 7
  • Codex produces poor outputs for concurrency at any specification abstraction and did not correctly synthesize solutions requiring fairness, atomicity, or both.Source: §2.3.1 · p. 7
  • Codex handles small constrained nondeterministic tasks and generates useful boilerplate for common machine-learning code, but nuanced or uncommon prompts may yield inaccurate outputs.The boilerplate may be easy for a user to tweak, and the paper notes potential to accelerate machine-learning model building.Source: §2.3.1 · p. 8
  • Codex is most effective on one-function or module-level tasks, while multi-module specifications requiring automatic architecture determination and unique higher-level prompts remain difficult.For module-level synthesis, correctness and construct selection are largely correlated with available data rather than specification abstraction or conciseness.Source: §2.3 · p. 6, §2.3.1 · p. 8
  • Codex does not guarantee correctness or soundness, may produce syntactically invalid or undefined code, struggles with long specifications, and lacks capability for complex hyperproperties such as cryptographic information-flow policies.Source: §2.3.1 · p. 8
  • The capabilities evaluation provides a generative-capabilities baseline to inform Codex evaluation and risk assessment, addressing capacities and failure modes that remain incompletely understood.Source: §2.3.1 · p. 8

3 HAZARD ANALYSIS AND RISK ASSESSMENT

The framework adapts hazard analysis to systems involving Codex-like models, using capability evaluations to inform a risk assessment illustrated with the Codex API.

3 HAZARD ANALYSIS AND RISK ASSESSMENT

  • The SHA-like approach surveys hazard sources across applications, alignment, system design and implementation, regulatory oversight, and economic and environmental impacts.The paper describes its approach as a preliminary System Hazard Analysis with further categorization and prioritization across subsystems.Source: §3 · p. 9
  • The authors propose new Hazard Severity Categories for language-model APIs, supported by defined harms and losses, to account for safety issues standard industry categories may miss.They intend the expanded definitions to support hazard analysis in machine learning and consideration of novel harms from LLM uses.Source: §3 · p. 9, Table 1 · p. 10
  • The risk model uses hazard severity and probability to prioritize hazards, but quantitative probability analysis is not always currently possible for novel Codex-like systems.The authors use qualitative probability levels and note that Codex API estimates draw on the Section 2 evaluations.Source: §3 · pp. 9–10
  • Crossing the hazard severity categories with qualitative hazard-probability levels forms the Hazard Risk Index used to compare hazards.The assessment uses the standard HRI metric, while distinguishing hazard sources from risk categories associated with model construction versus model application.Source: §3 · pp. 9–10
  • The Codex API example presents a simplified risk assessment of hazard sources, descriptions, and controls, with preliminary HRIs supporting comparisons about whether hazards merit control.The authors recommend that risk assessments be conducted by a multidisciplinary team.Source: §3 · p. 10

4 RISK ASSESSMENT OUTCOME

The risk assessment identifies pressing hazards across applications, alignment, system design, legal oversight, and economic and environmental impacts. It is a selective account, not an exhaustive inventory.

4 RISK ASSESSMENT OUTCOME

  • The reported hazards are a selective account: the authors say space constraints prevented an exhaustive list and analyses.They present the framework and most pressing risks as an aid for model builders assessing risks specific to their models.Source: §4.1 · p. 10

4.1 Application

  • Application hazards include biased or vulnerable completions and potential assistance with safety-critical technologies; the current Codex implementation’s risk is described as not high, but may increase as models advance.The authors also identify open-ended non-code use and use in neural-network development, including disinformation operations and surveillance, among application concerns.Source: §4.1 · pp. 11–12

4.2 Alignment

  • Alignment risks include reproducing mistakes or bugs in prompts and offering superficially correct solutions that miss user intent, potentially harming novice programmers’ productivity and learning.The examples include subtle or accidental mistakes in the input code or comments.Source: §4.2 · p. 13

4.3 System Design and Implementation

  • System-design risks include inadequate requirements or documentation, inaccessible interfaces, and over-trust in generated mission-critical content that can cause developers to miss safety-relevant details.The passage describes those details as ones manual processes might otherwise reveal.Source: §4.3 · p. 13

4.4 Regulatory and Legal Oversight

  • Legal and regulatory concerns include ambiguous liability for inadvertent intellectual-property or licensed-code use and the possibility that software use may fall under export controls.The export-control example concerns foreign-made items incorporating 25% or more controlled U.S.-origin content.Source: §4.4 · p. 13

4.5 Economic and Environmental Impacts

  • Economic and environmental risks include energy-intensive tools and uneven access that may widen economic opportunity gaps and demographic disparities among software developers.The passages also identify potential displacement of certain jobs and concentration of power through unequal access to tools and productivity gains.Source: §4.5 · pp. 13–14

5 HAZARD CONTROLS AND MITIGATIONS

The framework describes immediate and long-term controls to reduce hazards, but does not prescribe a universal mitigation priority. Selection should reflect system-specific factors and balance safety benefits against implementation costs.

5 HAZARD CONTROLS AND MITIGATIONS

  • Mitigation priorities should depend on system-specific factors, including local costs, trade-offs, and model capability levels.The paper does not recommend a specific prioritization among potential mitigations.Source: §5 · p. 14
  • The authors use ALARP as a selection basis: residual system risk should be as low as reasonably practicable.The principle balances realized safety benefits against implementation costs.Source: §5 · p. 14
  • Mitigations are divided into solutions feasible to implement today and longer-term solutions that may require open research or resources across the system life cycle.The paper notes that immediately feasible solutions may lessen hazards even when they do not reflect the severity of the risks they address.Source: §5 · p. 14

5.1 Plausible and Immediate Mitigations

The immediate-mitigation section covers communication, product and API controls, and data provenance. These options address user understanding, risky completions and usage, and the quality and governance of data.

5.1.1 Documentation and Communication Channels.

  • Documentation and communication channels can explain acceptable uses, safety risks, model limitations, data collection, expertise needs, and safety-critical certification scope.Channels for users and affected stakeholders can support concerns and reports of acceptable-use violations.Source: §5.1.1 · pp. 14–15

5.1.2 Product and API.

  • Product and API controls include filtering outputs, gating completions on user input, monitoring use, and applying rate limits or operational constraints.The paper also proposes code analysis, including syntactic checks and module-level formal verification; verification is not viable for dynamically typed languages, according to the supplied passage.Source: §5.1.2 · p. 15
  • Interfaces can prompt users to review generated code, while rate limits may reduce risk before hazards are well understood, even if they inhibit non-malicious development.The paper also proposes monitoring normal usage volumes to identify anomalous levels that might indicate malicious use.Source: §5.1.2 · pp. 15–16

5.1.3 Data Provenance.

  • Data provenance work should assess data quality, validity, availability, representation, and auditability, with oversight for access and sensitive-data use.The listed measures include identifying relevant training or test data and logging who accessed data, when, where, how, and why.Source: §5.1.3 · pp. 16–17

5.2 Long-Term Mitigations

Long-term mitigations span model architecture, training and classifiers, user and worker rights, and research on economic impacts. Their effectiveness remains an open question requiring evaluation and monitoring.

5.2.1 ML Architecture and Implementation. •

  • Aligning model outputs with programming-language syntax or ASTs could make them more amenable to security and safety techniques.The paper notes that considering syntax and semantics could also help distinguish code from natural-language completions.Source: §5.2.1 · p. 17

5.2.2 Fine-tuning, model re-training, and classifiers. •

  • Long-term model measures include curated fine-tuning, classifiers for malicious use and bias, and removing libraries that could accelerate hazards.The paper also suggests models that help users clarify ambiguous specifications, while stating that enforcing formal specifications would not be beneficial.Source: §5.2.2 · p. 17

5.2.3 User and worker rights.

  • A vulnerability disclosure program and bias bounty could bring users and workers into future hazard and safety discussions.The paper also proposes researching compensation for user data later used in training.Source: §5.2.3 · p. 18

5.2.4 Economic impacts. •

  • Research and external partnerships are proposed to forecast economic and labor-market impacts and examine how they may translate into hazards.How much each control reduces hazard risk remains open; the paper calls for qualitative or quantitative evaluation and verification and monitoring of mitigations.Source: §5.2.4 · p. 18

6 CONCLUSIVE REMARKS AND LOOKING FORWARD

The conclusion argues that code-generation evaluation should move beyond toy synthesis tasks and assess capability relative to human ability. It presents a hazard analysis and mitigation framework, while emphasizing ongoing evaluation in context and in the real world.

6 CONCLUSIVE REMARKS AND LOOKING FORWARD

  • The proposed evaluation framework examines code-synthesis capacity against specification complexity and expressivity, and whether models understand and execute specifications relative to human ability.The paper identifies evaluation beyond toy function synthesis as necessary.Source: §6 · p. 18
  • The hazard analysis outlines pressing risks applicable to code-synthesis LLMs and controls or mitigations for model creators to consider.The analysis addresses risks that may arise technically, socially, politically, and economically.Source: §6 · p. 18
  • Model evaluation should continue during safe development and deployment, including in specific use contexts and the real world.Source: §6 · p. 18
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