The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs | Paper Citation Record

Observation 973018c6-40ad-40a0-86cb-59a24a43963c · outbound

This paper cites Evaluating Large Language Models Trained on Code.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Evaluating Large Language Models Trained on Code

Reference 1

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Observation 8883c84a-8e6b-4070-80ed-7476b7193265 · outbound

This paper cites A survey on large language models for code generation,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs A survey on large language models for code generation,

Reference 2

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Observation 782d29e9-dfe7-4423-b202-9cd7b0622460 · outbound

This paper cites Fairness set and forgotten: Mining fairness toolkit usage in open-source machine learning projects,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Fairness set and forgotten: Mining fairness toolkit usage in open-source machine learning projects,

Reference 3

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Observation 494f76d5-8a8e-4604-ab00-75f64c3c46d9 · outbound

This paper cites Contextual fairness-aware practices in ml: A cost-effective empirical evaluation,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Contextual fairness-aware practices in ml: A cost-effective empirical evaluation,

Reference 4

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Observation 44e7f9ec-dbd0-4219-9b6c-3e256d239dfe · outbound

This paper cites Fairness on a budget, across the board: A cost-effective eval- uation of fairness-aware practices across contexts, tasks, and sensitive attributes,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Fairness on a budget, across the board: A cost-effective eval- uation of fairness-aware practices across contexts, tasks, and sensitive attributes,

Reference 5

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Observation 5377f9de-e30b-49a0-af2e-f0df9c1b05d3 · outbound

This paper cites Fair and square? evaluating fairness of llm-generated synthetic datasets,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Fair and square? evaluating fairness of llm-generated synthetic datasets,

Reference 6

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Observation 886f0db0-100d-4a52-8c18-5344550cc9b8 · outbound

This paper cites Human-written vs. ai- generated code: A large-scale study of defects, vulnerabilities, and complexity,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Human-written vs. ai- generated code: A large-scale study of defects, vulnerabilities, and complexity,

Reference 7

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Observation 18d1941c-00ac-490d-9e40-f5d9667e74f2 · outbound

This paper cites Security vulnerabilities in ai-generated code: A large-scale analysis of public github repositories,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Security vulnerabilities in ai-generated code: A large-scale analysis of public github repositories,

Reference 8

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Observation 88b65608-5269-4491-8cbd-b1278aa91cc7 · outbound

This paper cites Security degradation in iterative ai code generation: A systematic analysis of the paradox,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Security degradation in iterative ai code generation: A systematic analysis of the paradox,

Reference 9

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Observation 3a3ea648-cc69-42a3-97db-fa67750289ee · outbound

This paper cites Just another copy and paste? comparing the security vulnerabilities of chatgpt generated code and stackoverflow answers,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Just another copy and paste? comparing the security vulnerabilities of chatgpt generated code and stackoverflow answers,

Reference 10

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Observation 9c3bcbc9-474a-4272-9ea1-e0a0e8bdb46c · outbound

This paper cites Do users write more insecure code with ai assistants?.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Do users write more insecure code with ai assistants?

Reference 11

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Observation f7ace93a-12c0-4792-8a00-e6f72efceb54 · outbound

This paper cites Asleep at the keyboard? assessing the security of github copilot’s code con- tributions,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Asleep at the keyboard? assessing the security of github copilot’s code con- tributions,

Reference 12

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Observation dba07129-b28c-48da-811a-1bc5a8547195 · outbound

This paper cites When code smells meet ml: on the lifecycle of ml-specific code smells in ml-enabled systems,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs When code smells meet ml: on the lifecycle of ml-specific code smells in ml-enabled systems,

Reference 13

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Observation 2fa6a33e-adf3-47a7-86ca-e735dd2ac5cf · outbound

This paper cites Into the ml-universe: An improved classification and characterization of machine-learning projects,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Into the ml-universe: An improved classification and characterization of machine-learning projects,

Reference 14

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Observation b788a624-6c6a-4937-bb9c-f3346672f757 · outbound

This paper cites An evidence- based study on the relationship of software engineering practices on code smells in python ml projects,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs An evidence- based study on the relationship of software engineering practices on code smells in python ml projects,

Reference 15

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Observation 739a6024-b548-4ad1-8f47-dfe0f2ae19db · outbound

This paper cites Under- standing developer practices and code smells diffusion in ai-enabled software: A preliminary study.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Under- standing developer practices and code smells diffusion in ai-enabled software: A preliminary study

Reference 16

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Observation 5ec99db1-4aa1-45aa-865d-11399d90655a · outbound

This paper cites Benchmarking prompt engineering techniques for secure code generation with gpt models,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Benchmarking prompt engineering techniques for secure code generation with gpt models,

Reference 17

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Observation 24f395cc-2044-4b73-bb7d-49fa4b940cde · outbound

This paper cites Llmseceval: A dataset of natural language prompts for security evaluations,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Llmseceval: A dataset of natural language prompts for security evaluations,

Reference 18

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Observation 8e208f7e-7db7-4b29-ac45-1affe91bdb11 · outbound

This paper cites Prompting techniques for secure code generation: A systematic investi- gation,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Prompting techniques for secure code generation: A systematic investi- gation,

Reference 19

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Observation 766750b5-e0c2-431f-8c92-efeacbdc80ec · outbound

This paper cites Retrieve, refine, or both? using task-specific guidelines for secure python code generation,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Retrieve, refine, or both? using task-specific guidelines for secure python code generation,

Reference 20

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Observation 7703af4c-7940-4674-a6a5-60e12532cfa5 · outbound

This paper cites Prompt variability effects on llm code generation,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Prompt variability effects on llm code generation,

Reference 21

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Observation 4660baeb-abac-4258-a008-279eb7b3bb46 · outbound

This paper cites Nlperturbator: Studying the robustness of code llms to natural language variations,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Nlperturbator: Studying the robustness of code llms to natural language variations,

Reference 22

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Observation 1b5876fc-1ca9-4d43-803d-01997105de05 · outbound

This paper cites Toward measuring prompt quality: A preliminary investigation on prompt smells,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Toward measuring prompt quality: A preliminary investigation on prompt smells,

Reference 23

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Observation 1d67c6b6-8433-4a90-94e0-ce90dcaba13c · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 24

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Observation 1d0cee4f-b084-4bd5-869c-7ac265e44123 · outbound

This paper cites Using large language models to support software engineering documentation in waterfall life cycles: Are we there yet?.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Using large language models to support software engineering documentation in waterfall life cycles: Are we there yet?

Reference 25

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Observation 7cdfb9ee-6557-42e5-978a-5daa9a0b3259 · outbound

This paper cites Does the grammatical structure of prompts influence the responses of generative artificial intelligence? an exploratory analysis in spanish,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Does the grammatical structure of prompts influence the responses of generative artificial intelligence? an exploratory analysis in spanish,

Reference 26

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Observation df19b726-4099-4e5d-a5da-0d211cf00da5 · outbound

This paper cites Security and privacy challenges of large language models: A survey,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Security and privacy challenges of large language models: A survey,

Reference 27

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Observation 2bf80a29-07cb-4196-b568-d48856c6889c · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs On the Opportunities and Risks of Foundation Models

Reference 28

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Observation 45ad1d07-02bb-4d39-adb7-9e85d3faff2a · outbound

This paper cites Cicalese, A.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Cicalese, A

Reference 29

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Observation 51d3239a-4ec8-40b6-a2bb-d761d44dd7a9 · outbound

This paper cites EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers

Reference 30

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Observation b3e7246a-56b6-428a-96fb-d561c210d739 · outbound

This paper cites Sallm: Security assessment of generated code,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Sallm: Security assessment of generated code,

Reference 31

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Observation e72ff148-98f6-415c-9521-fc3ecc9a9d5b · outbound

This paper cites Devgpt: Studying developer-chatgpt conversations,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Devgpt: Studying developer-chatgpt conversations,

Reference 32

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Observation 6a0e4cd7-ec3a-4a87-9e23-508a24992cf3 · outbound

This paper cites Do prompt patterns affect code quality? a first empirical assessment of chatgpt-generated code,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Do prompt patterns affect code quality? a first empirical assessment of chatgpt-generated code,

Reference 33

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This paper cites Unlocking code simplicity: The role of prompt patterns in managing llm code complexity,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Unlocking code simplicity: The role of prompt patterns in managing llm code complexity,

Reference 34

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Observation b2121237-2cd3-4146-b148-a0d035f1c3c6 · outbound

This paper cites Selective Prompt Anchoring for Code Generation.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Selective Prompt Anchoring for Code Generation

Reference 35

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Observation 0257f690-dae0-423e-9357-e102905fb375 · outbound

This paper cites Wohlin, P.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Wohlin, P

Reference 36

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Observation d667298e-2c3c-4889-8ba4-ada21142d4f1 · outbound

This paper cites Fast and accurate neural crf constituency parsing,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Fast and accurate neural crf constituency parsing,

Reference 38

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Observation 3466c980-4c2a-4fa8-90de-c24ff7089562 · outbound

This paper cites Grips: Gradient-free, edit-based instruction search for prompting large language models,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Grips: Gradient-free, edit-based instruction search for prompting large language models,

Reference 39

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Observation 9fcbfc6d-7081-44c4-bd32-7cb369bb3ca9 · outbound

This paper cites Hierarchical fine-grained state-aware graph attention network for dialogue state tracking,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Hierarchical fine-grained state-aware graph attention network for dialogue state tracking,

Reference 40

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Observation d189c679-2c7e-489e-8fe5-9e18cb633140 · outbound

This paper cites Available: https://surdeanu.cs.arizona.edu/mihai/teaching/ ista555-fall13/readings/PennTreebankConstituents.html.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Available: https://surdeanu.cs.arizona.edu/mihai/teaching/ ista555-fall13/readings/PennTreebankConstituents.html

Reference 41

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Observation 48285b1c-6a8c-4298-8dbb-919533a7da7a · outbound

This paper cites Building a large annotated corpus of English: The Penn Treebank,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Building a large annotated corpus of English: The Penn Treebank,

Reference 42

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Observation 1653a589-5492-404a-87eb-e38587d29be1 · outbound

This paper cites An answer recommendation algo- rithm based on semantic fusion heterogeneous information network,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs An answer recommendation algo- rithm based on semantic fusion heterogeneous information network,

Reference 43

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This paper cites Text semantic representation based on knowledge graph correction,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Text semantic representation based on knowledge graph correction,

Reference 44

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Observation 165408ea-a721-468b-b17b-f95437e3b56b · outbound

This paper cites Knowledge graph- based hierarchical text semantic representation,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Knowledge graph- based hierarchical text semantic representation,

Reference 45

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Observation b5d4d56b-4d68-44fb-848b-441953c867e7 · outbound

This paper cites Codelm- sec benchmark: Systematically evaluating and finding security vulnera- bilities in black-box code language models,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Codelm- sec benchmark: Systematically evaluating and finding security vulnera- bilities in black-box code language models,

Reference 46

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Observation d1f5f058-5acf-4252-bc1e-deae12105630 · outbound

This paper cites An exploratory study on fine-tuning large language models for secure code generation,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs An exploratory study on fine-tuning large language models for secure code generation,

Reference 47

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Observation 33860f5a-e67e-456f-a050-2d816901cbd4 · outbound

This paper cites Lost in the middle: How language models use long contexts,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Lost in the middle: How language models use long contexts,

Reference 48

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Observation 97cc6820-b72c-4eff-9eea-a87ac233da4f · outbound

This paper cites Empirical analysis of security vulnerabilities in python packages,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Empirical analysis of security vulnerabilities in python packages,

Reference 49

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Observation 3bd173ac-0692-4a43-bdc5-8d757c8ac0b5 · outbound

This paper cites Veracode uncovers the top security issues facing specific programming languages,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Veracode uncovers the top security issues facing specific programming languages,

Reference 50

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Observation c1607253-f9d5-4914-b8c2-61c93a9f0232 · outbound

This paper cites Shift to memory-safe languages gains momen- tum,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Shift to memory-safe languages gains momen- tum,

Reference 51

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Observation 210d2eb5-4bc7-491a-8c31-071459f70dbf · outbound

This paper cites Software vulnerability analysis across programming language and program representation landscapes: A survey,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Software vulnerability analysis across programming language and program representation landscapes: A survey,

Reference 52

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Observation 41a29c33-6209-47d9-a4c1-3ff7a4e62efc · outbound

This paper cites Systematic review: Analysis of coding vulnerabilities across languages,.

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs Systematic review: Analysis of coding vulnerabilities across languages,

Reference 53

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