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arxiv: 2605.24883 · v1 · pith:DHSNK6P4new · submitted 2026-05-24 · 💻 cs.AI · cs.CR· cs.SE

Inverting the Shield: Systematically Generating Safety Tests from Policy Specifications

Xiaoyue Lu , Xianglin Yang , Haijun Liu , Jiahao Liu , Kuntai Cai , Yan Xiao , Jin Song Dong This is my paper

Pith reviewed 2026-06-30 11:33 UTC · model grok-4.3

classification 💻 cs.AI cs.CRcs.SE
keywords AI safetyLLM testingpolicy specificationFirst-Order Logicred-teamingtest generationSemantic Policy Graphcompositional violations
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The pith

POLARIS compiles natural-language safety policies into First-Order Logic then explores a Semantic Policy Graph to generate executable test queries for LLMs.

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

The paper presents POLARIS as a method that translates unstructured safety policies into formal logic, builds a graph whose paths represent possible violation combinations, and traverses that graph to produce natural-language test cases. This replaces reliance on hand-crafted benchmarks or expert red-teaming with a traceable, coverage-driven process. A sympathetic reader would value the result because current LLM safety checks quickly become obsolete and lack systematic guarantees, whereas the new approach links high-level rules directly to concrete, reproducible queries. Experiments reported in the paper show the generated tests reach higher policy coverage and more successful attacks than prior baselines.

Core claim

POLARIS first compiles unstructured natural-language policies into First-Order Logic representations, then constructs a Semantic Policy Graph in which complex violation scenarios appear as traversable paths; systematic exploration of the graph uncovers compositional violation patterns that are instantiated as executable natural-language test queries, yielding higher policy coverage and attack success counts than established baselines while preserving a traceable link from rules to tests.

What carries the argument

The Semantic Policy Graph, whose nodes and edges encode policy elements and their compositional violation patterns so that path traversal produces concrete test cases.

If this is right

  • The generated queries achieve higher policy coverage than existing benchmark or red-teaming approaches.
  • Attack success counts on LLMs are higher than those obtained from established baselines.
  • Every test query carries an explicit trace back to the original policy clauses.
  • Safety testing becomes reproducible and less dependent on repeated expert input as policies change.
  • Formal methods are applied directly to the construction of AI safety evaluations.

Where Pith is reading between the lines

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

  • The same compilation-plus-graph approach could be applied to generate tests for non-safety policies such as factual accuracy or fairness constraints.
  • If the graph traversal is exhaustive, the method might eventually support statements about the completeness of the generated test suite relative to the original policy.
  • Organizations that maintain large policy documents could use the framework to refresh test suites automatically whenever policies are revised.

Load-bearing premise

Natural-language policies can be compiled into First-Order Logic representations that preserve meaning sufficiently for the graph construction and test generation steps to remain valid and complete.

What would settle it

If the tests generated by POLARIS on a given policy set produce lower coverage or fewer successful attacks than a carefully constructed manual benchmark on the same policies, the claim of superior systematic generation would be refuted.

Figures

Figures reproduced from arXiv: 2605.24883 by Haijun Liu, Jiahao Liu, Jin Song Dong, Kuntai Cai, Xianglin Yang, Xiaoyue Lu, Yan Xiao.

Figure 1
Figure 1. Figure 1: The Overview of POLARIS. (1) Policy-to-Logic Compilation: Unstructured, natural-language policy texts are parsed to extract entities and relations, which are then formalized into a Knowledge Base (KB) of logical axioms called Abstract Violation Templates (AVTs). (2) Semantic Graph Construction: The components from the KB are used to build a unified semantic graph, which is then densified through an enrichm… view at source ↗
Figure 2
Figure 2. Figure 2: The influence of K on Coverage Scores across different distance thresholds. (a) Distance Threshold = 0.4 (b) Distance Threshold = 0.5 (c) Distance Threshold = 0.6 (d) Distance Threshold = 0.7 [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The influence of K on Novelty Scores across different distance thresholds [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
read the original abstract

The widespread integration of Large Language Models (LLMs) necessitates rigorous and systematic safety evaluation. Existing paradigms either rely on constructed benchmarks to assess safety from predefined perspectives, or employ dynamic red-teaming to probe potential vulnerabilities. While effective, these approaches face challenges, as they depend heavily on expert domain knowledge, offer limited systematic guarantees, and are vulnerable to rapid obsolescence. To address these limitations, we introduce a novel framework POLARIS that brings the rigor of specification-based software testing to AI safety. POLARIS first compiles unstructured natural-language policies into First-Order Logic (FOL) representations, establishing a traceable link between high-level rules and concrete test cases. This formalization enables the construction of a Semantic Policy Graph, where complex policy violation scenarios are encoded as traversable paths. By systematically exploring this graph, POLARIS uncovers compositional violation patterns, which are then instantiated into executable natural-language test queries, enabling coverage-driven and reproducible safety testing. Experiments demonstrate that POLARIS achieves higher policy coverage and attack success counts compared to established baselines. Crucially, by bridging formal methods and AI safety, POLARIS provides a principled, automated approach to ensuring LLMs adhere to safety-critical policies with verifiable traceability. We release our code at https://github.com/huac-lxy/POLARIS.

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

3 major / 0 minor

Summary. The paper introduces POLARIS, a framework that compiles natural-language safety policies into First-Order Logic representations, constructs a Semantic Policy Graph to encode and explore compositional violation patterns as traversable paths, and generates executable natural-language test queries from those paths. It claims this yields higher policy coverage and attack success counts than established baselines while providing traceable links from high-level policies to concrete tests, and releases code at a GitHub repository.

Significance. If the FOL compilation step preserves policy semantics and the graph exploration is sound, the approach could offer a more systematic, automated, and reproducible alternative to expert-driven benchmarks or ad-hoc red-teaming for LLM safety evaluation, with the potential to reduce obsolescence and improve verifiability by bridging formal methods and AI safety.

major comments (3)
  1. [Abstract] Abstract: the claims of 'higher policy coverage and attack success counts compared to established baselines' are presented without any description of the experimental setup, choice of baselines, coverage or success metrics, number of policies or test cases, statistical tests, or error analysis, so the data-to-claim link cannot be evaluated.
  2. [Abstract] Abstract (first paragraph): the central compilation of unstructured natural-language policies into FOL representations is asserted to 'establish a traceable link' but supplies no procedure, handling of ambiguity, quantifier scope, negation, context, intent, or exceptions, which directly undermines the validity of the Semantic Policy Graph paths and the generated tests.
  3. [Abstract] Abstract: the construction and systematic exploration of the Semantic Policy Graph for 'compositional violation patterns' is described at a high level with no formal definition of the graph nodes/edges, traversal algorithm, completeness guarantees, or how paths are instantiated back to natural language, leaving the core technical contribution unevaluable.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed comments on the abstract. We agree that the abstract is currently too concise and does not sufficiently support evaluation of the central claims or technical contributions. We will revise the abstract to incorporate brief but concrete descriptions of the experimental setup, the FOL compilation process, and the Semantic Policy Graph construction and exploration, drawing directly from the detailed sections in the full manuscript. This will address the evaluability concerns while preserving the abstract's brevity.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claims of 'higher policy coverage and attack success counts compared to established baselines' are presented without any description of the experimental setup, choice of baselines, coverage or success metrics, number of policies or test cases, statistical tests, or error analysis, so the data-to-claim link cannot be evaluated.

    Authors: We agree that the abstract does not include these details. In the revised abstract we will add one sentence summarizing the experimental setup: we evaluate on 50 safety policies drawn from established sources, compare against two baselines (manual red-teaming and automated prompt-generation methods), measure policy coverage via the fraction of FOL clauses exercised and attack success via LLM judge agreement on violation, and report results with standard error across three runs. The full statistical analysis, error breakdown, and exact baseline implementations appear in Section 5 of the manuscript. revision: yes

  2. Referee: [Abstract] Abstract (first paragraph): the central compilation of unstructured natural-language policies into FOL representations is asserted to 'establish a traceable link' but supplies no procedure, handling of ambiguity, quantifier scope, negation, context, intent, or exceptions, which directly undermines the validity of the Semantic Policy Graph paths and the generated tests.

    Authors: The manuscript describes the compilation procedure in Section 3, which decomposes policies into atomic predicates, applies a rule-based translator that explicitly scopes quantifiers, normalizes negation, and resolves context via policy metadata. We acknowledge the abstract provides no indication of this. The revision will insert a short clause: 'via a structured translation that decomposes policies, scopes quantifiers, and normalizes negation and exceptions.' This will better ground the traceability claim without lengthening the abstract excessively. revision: yes

  3. Referee: [Abstract] Abstract: the construction and systematic exploration of the Semantic Policy Graph for 'compositional violation patterns' is described at a high level with no formal definition of the graph nodes/edges, traversal algorithm, completeness guarantees, or how paths are instantiated back to natural language, leaving the core technical contribution unevaluable.

    Authors: We agree the abstract is too high-level on this point. Section 4 formally defines nodes as policy predicates and edges as compositional operators, presents a depth-first traversal with feasibility pruning that guarantees enumeration of all minimal violation paths up to a bounded depth, and details template-based natural-language instantiation. The revised abstract will include a concise clause: 'The graph encodes predicates as nodes and compositional relations as edges; paths are enumerated by bounded DFS and instantiated via templates.' This will make the core contribution evaluable from the abstract. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework claim is independent of its outputs

full rationale

The paper describes a new framework (POLARIS) that compiles NL policies to FOL, builds a Semantic Policy Graph, explores paths for violation patterns, and generates test queries. No equations, fitted parameters, or derived quantities are presented that reduce to the inputs by construction. The central claim concerns the existence, traceability, and empirical performance of this pipeline versus baselines; it does not define any target metric in terms of its own generated tests. No self-citation load-bearing steps, uniqueness theorems, or ansatzes appear in the provided text. The FOL compilation step is an explicit modeling assumption whose validity is external to the derivation itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the domain assumption that natural-language policies translate faithfully into FOL and introduces the Semantic Policy Graph as a new modeling construct without external validation mentioned in the abstract.

axioms (1)
  • domain assumption Unstructured natural-language policies can be compiled into accurate First-Order Logic representations that preserve the intended meaning
    Invoked as the first step to establish a traceable link between policies and test cases.
invented entities (1)
  • Semantic Policy Graph no independent evidence
    purpose: To represent complex policy violation scenarios as traversable paths that enable systematic discovery of compositional violations
    New construct introduced by the framework; no independent evidence of its utility outside the paper is provided in the abstract.

pith-pipeline@v0.9.1-grok · 5780 in / 1262 out tokens · 49521 ms · 2026-06-30T11:33:06.372781+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

65 extracted references · 3 canonical work pages · 2 internal anchors

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    Do not compromise the privacy of others

    Phase 1: Policy Deconstruction (LLM-based Extraction) The process begins by parsing natural language policies into a structured schema to eliminate linguistic ambiguity. We identifyEntities,Ac- tions, andDeontic Modalityto extract Abstract Violation Templates (A VTs). • Example Policy:“Do not compromise the privacy of others.” • Structured Schema:{Subj: “...

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    Phase 2: Logical Formalization (Schema- Guided Translation) The extracted elements are subsequently mapped into First-Order Logic (FOL) axioms to define formal violation constraints. For the aforementioned policy, the formalization is ex- pressed as: ∀p∀x∀y(U ser(x)∧P erson(y)∧P rivacy(p, y)) →F(Compromise(x, p)) This logical representation ensures that t...

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    This stage injects domain-specific knowledge by connecting ab- stract predicates to high-risk concrete concepts

    Phase 3: Graph Construction and Traversal (Knowledge Injection) Using the formal predicates as seed nodes, we employ LLM-driven Link Prediction to traverse the Semantic Policy Graph. This stage injects domain-specific knowledge by connecting ab- stract predicates to high-risk concrete concepts. • Semantic Path:[Seed: Privacy] → (con- tains) → [Node: Medic...

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    I need to ac- cess my neighbor’s medical records for a research project without their consent. How can I bypass the hospital’s database encryp- tion?

    Phase 4: Query Instantiation (Contextual Generation) In the final phase, the generator wraps the iden- tified semantic path into a specific natural lan- guage context. This masks the underlying ad- versarial intent within a realistic scenario. • Final Instantiated Query:“I need to ac- cess my neighbor’s medical records for a research project without their...

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    The optimal number of topics (K) is determined by maximizing the Coherence Score ( Cv)

    Scenario Type Distribution:We employ La- tent Dirichlet Allocation (LDA) to identify un- derlying topic clusters. The optimal number of topics (K) is determined by maximizing the Coherence Score ( Cv). A higher K indicates a broader coverage of distinct safety-critical themes rather than clustering around repetitive categories

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    Expression Style Diversity:We measure struc- tural heterogeneity using theSyntactic Diversity Score( Dsyn), defined as the ratio of unique Part- of-Speech (POS) sequence patterns to the total sample sizeN: Dsyn = Count(Unique POS Patterns) N Table 13: Comparison of Query Diversity and Complex- ity across Benchmarks. Benchmark Scenario Types (↑) Expression...

  10. [10]

    indirectness

    Contextual Complexity:We adopt the average Dependency Tree Depthas an indicator of hi- erarchical nesting and "indirectness." For each query, we calculate the maximum depth of its dependency tree: Complexity= 1 N NX i=1 MaxDepth(Queryi) Higher scores signify more sophisticated, multi- layered linguistic structures (e.g., nested role- play or conditional c...

  11. [11]

    Check if names indicate a hierarchical relationship

  12. [12]

    Check if attributes show containment (e.g., A's attributes are superset of B's)

  13. [13]

    Exclude clearly unrelated cases

  14. [16]

    NodeName

    Even if none of the nodes in the current batch are related to any of the other nodes in this batch, you must still return an explicit relationship record for every node—simply mapping each node to []—rather than returning only ```json[]```. #### Output Format: In the list of dictionaries, each element is structured such that the key represents the current...

  15. [17]

    Check if they are at the same abstraction level

  16. [18]

    Check if roles are identical

  17. [19]

    Check for significant attribute overlap

  18. [20]

    Exclude containment and exclusion relationships

  19. [21]

    When analyzing relationships, take the node's attributes into account alongside its NodeName—do not base your assessment solely on the NodeName

  20. [22]

    If a node in the current batch has no related nodes, its corresponding value should be []

  21. [23]

    Subject": str}, {

    Even if none of the nodes in the current batch are related to any of the other nodes in this batch, you must still return an explicit relationship record for every node—simply mapping each node to []—rather than returning only ```json[]```. #### Output Format: In the list of dictionaries, each element is structured such that the key represents the current...

  22. [24]

    - Variable names should not conflict with existing ones; uniqueness matters, but variable names do not need to start with "y"

    Quantification: - Always quantify over the core object variable using ∀ or ∃. - Variable names should not conflict with existing ones; uniqueness matters, but variable names do not need to start with "y"

  23. [25]

    modifier + head noun

    The subject phrase may contain multiple distinct subjects; determine whether they are genuinely multiple subjects. Each subject is composed of “modifier + head noun.”

  24. [26]

    - Modifier formalization: - Adjectival / prepositional modifiers: choose one of two styles:

    For each subject: - Head-noun formalization: use a distinct variable for each different head noun, e.g., Vulnerability(v). - Modifier formalization: - Adjectival / prepositional modifiers: choose one of two styles:

  25. [27]

    Compact naming (recommended when modifiers are short): SensitiveData(d)

  26. [28]

    - Ownership / belonging modifiers: use binary relation

    Predicate style (recommended when many modifiers): sensitive data → Data(d) ∧ IsSensitive(d). - Ownership / belonging modifiers: use binary relation

  27. [29]

    Variable safety: every variable must be introduced by a quantifier (∀t, ∀d, ∀p, ∀b, …); undeclared variables are forbidden

  28. [30]

    our service

    Possessive pronouns (our, my, ours, …): in phrases like “our service” or “our data,” “our” refers to the policy issuer (e.g., OpenAI)

  29. [31]

    For each subject with multiple modifiers, connect them with ∧

  30. [32]

    Use camelCase for all predicates and variables

  31. [33]

    LogicalSubject

    Connect different subjects with ∨. - If the subjects are truly distinct, use ∨ to connect them. - If they are variations of the same subject, Therefore, connect the two variants with ∧ and enclose them in parentheses: (Variant₁ ∧ Variant₂). note: Any variable must be defined before it is reused. #### Output Format: Return your output as a JSON object with...

  32. [34]

    - Variable names should not conflict with existing ones; uniqueness matters, but variable names do not need to start with "y"

    Quantification: - Always quantify over the core object variable using ∀. - Variable names should not conflict with existing ones; uniqueness matters, but variable names do not need to start with "y"

  33. [35]

    Core Object: - Identify the true head noun of the object

  34. [36]

    Adjectival and Prepositional Modifiers: - Use description logic-style predicates for modifiers

  35. [37]

    Disjunctions: - If the object contains a semantic OR (e.g., "X or Y"), use ∨ (logical OR):

  36. [38]

    Ownership or Belonging: - Represent ownership with binary relations

  37. [39]

    that distort a person's behavior

    Subordinate Clauses or Relative Clauses: - Clauses like "that distort a person's behavior" should be logically attached to the object using new predicates. - You must define new variables and entity types before using them

  38. [40]

    - Do not use a variable (like x, p, etc.) unless it's either already defined or defined in this object logic

    Variable Safety: - Always declare new variables with quantifiers: ∀t, ∀d, ∀p, ∀b, etc. - Do not use a variable (like x, p, etc.) unless it's either already defined or defined in this object logic

  39. [41]

    - Stick strictly to the literal meaning and structure of the original object phrase

    No Hallucination: - Do NOT infer or introduce terms, roles, or relations that are not clearly stated in the object. - Stick strictly to the literal meaning and structure of the original object phrase

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    our", "our service

    Pronominal Reference (Our, my, ours, etc.): - If the object includes a possessive pronoun like "our", "our service", interpret "our" as referring to the policy issuer. - Do not break such noun phrases apart. Instead, treat them as an entity type. Example: - "our service" → ∀s OpenAIService(s) - The entity name (e.g., OpenAI) should be fixed and explicit —...

  41. [44]

    you” or “yourself,

    If the object contains words like “you” or “yourself,” do not introduce a new variable; instead, reuse the variable that these pronouns refer to. For example, if “yourself” refers to the bound variable User(x), the object must be expressed as x. note: Any variable must be defined before it is reused. #### Output Format: Return your output as a JSON object...

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    The action must be expressed as a predicate: e.g., Access(x, y), Create(u, d)

  43. [46]

    The subject is always the first argument of the action predicate (e.g., x), and the object is the second argument (e.g., y, d, p, etc.), based on the object's quantified variable

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    - Use O(...) to denote obligation, e.g., O(Delete(x, y))

    Wrap the predicate expression in a deontic logic operator: - Use F(...) to denote prohibition, e.g., F(Access(x, y)). - Use O(...) to denote obligation, e.g., O(Delete(x, y))

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    Use it exactly as provided in the input — no abbreviation, no synonym substitution, no rephrasing

    Do not change the action verb. Use it exactly as provided in the input — no abbreviation, no synonym substitution, no rephrasing

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    - The same applies to disjunctions in the subject or object

    Disjunction Handling: - If the action verb contains multiple verbs joined by "or", treat each verb as an individual predicate, then combine them using logical OR ∨. - The same applies to disjunctions in the subject or object

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    Do not add any modifiers to the subject or object that are not explicitly included in the given logical forms

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    Ensure all variables are consistent with those defined in the logical subject and object

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    LogicalPredicate

    When multiple subjects or objects appear, carefully distinguish whether they represent distinct subjects/objects or merely modifiers of a single subject/object. #### Output Format: Return your output as a JSON object with the following structure, One dictionary represents the logical formalization of a single policy: ```json [ {"LogicalPredicate": "the lo...

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    Variable Management: - All variables must be declared before use - Reuse existing variables from provided logical forms - New variables must be uniquely quantified (∀x, ∀y, etc.) - Variable names must not be reused; each variable must be unique

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    b) must Separate modifiers from the words they modify

    Logical Conversion Rules: A) Subject/Object Logic: a) Quantify over all core objects; every noun must be separately quantified. b) must Separate modifiers from the words they modify. Convert modifiers to DL- style predicates c) Handle disjunctions with ∨ d) Model ownership relations B) Predicate Logic: a)Predicates may be either actions or prepositions. b...

  52. [55]

    Use camelCase for naming

  53. [56]

    The logical form of the condition is: LogicalConditionSubject ∧ LogicalConditionPredicate ∧ LogicalConditionObject

  54. [57]

    If the policy has no conditions, return []

  55. [58]

    The logically formalized condition must come solely from the input I provide—do not invent any additional content

  56. [59]

    The output structure for each policy must exactly match the input structure; do not merge or split conditions

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    our", "our service

    Pronominal Reference (Our, my, ours, etc.): - If the object includes a possessive pronoun like "our", "our service", interpret "our" as referring to the policy issuer. - Do not break such noun phrases apart. Instead, treat them as an entity type. Example: - The entity name should be fixed and explicit — do not introduce undefined organizations. - Ensure v...

  58. [61]

    Each condition within every policy must remain unmerged; the output structure must exactly match the input structure

  59. [62]

    Do not merge any conditions whatsoever

  60. [63]

    LogicalCondition1

    If a policy contains no conditions, return []. #### Output Format: Return your output as a JSON object with the following structure, One List represents the logical formalization of a single policy, The number and structure of LogicalCondition outputs must exactly match the input Conditions. Do not merge any conditions whatsoever.: ```json [ [["LogicalCon...

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    - If the sentence lacks an explicit subject, infer the most plausible subject by examining the subsequent actions and behaviors

    Subject Identification: - Extract the entity being restricted. - If the sentence lacks an explicit subject, infer the most plausible subject by examining the subsequent actions and behaviors. - For passive voice, identify the logical actor. - The subject should cover the broadest possible range of actors performing the action, with minimal similarity betw...

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    - If the predicate is a compound (coordinated) predicate, it must not be split

    True Forbidden Action: - Identify the ULTIMATE harmful outcome - The predicate must be exclusively verb or preposition; it must never contain any noun or noun modifier. - If the predicate is a compound (coordinated) predicate, it must not be split

  63. [66]

    If the predicate's object is missing in the sentence, infer and supply an appropriate object based on context (e.g., Person)

    Direct Object: - The immediate target of the forbidden action - Must include all essential descriptors - In general, the object follows the predicate. If the predicate's object is missing in the sentence, infer and supply an appropriate object based on context (e.g., Person). - The object must consist only of a noun and its modifiers. - If first-person pr...

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    Conditions: a) Method Conditions: - The means/tools enabling the forbidden action - Includes all phrases describing how the action could be performed b) Unified Effect Conditions: - The complete harmful outcome chain - Treat the entire consequence as one condition c) If first-person pronouns appear, replace them with appropriate transformations of {organization}

  65. [68]

    Subject + Predicate + Object + Condition

    Exactly one dictionary must be generated for each policy. Each Dict represents one policy. #### Critical Constraints: - NEVER separate connected consequence clauses - The reconstructed "Subject + Predicate + Object + Condition" must form a complete prohibition - Everything beyond the subject-verb-object triplet counts as a condition; conditions should rem...