# SALLM: Security Assessment of Generated Code

arXiv: https://arxiv.org/abs/2311.00889 · HTML: https://mlsanity.com/paper/2311.00889/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 78 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [Measuring Coding Challenge Competence With APPS](https://mlsanity.com/paper/2105.09938/) (2021)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation](https://mlsanity.com/paper/2211.11501/) (2022)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [The Stack: 3 TB of permissively licensed source code](https://mlsanity.com/paper/2211.15533/) (2022)
- [CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X](https://mlsanity.com/paper/2303.17568/) (2023)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [SPoC: Search-based Pseudocode to Code](https://mlsanity.com/paper/1906.04908/) (2019)
- [Multi-lingual Evaluation of Code Generation Models](https://mlsanity.com/paper/2210.14868/) (2022)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [CodeGen2: Lessons for Training LLMs on Programming and Natural Languages](https://mlsanity.com/paper/2305.02309/) (2023)
- [Mapping Language to Code in Programmatic Context](https://mlsanity.com/paper/1808.09588/) (2018)
- [Productivity Assessment of Neural Code Completion](https://mlsanity.com/paper/2205.06537/) (2022)
- [Code Prediction by Feeding Trees to Transformers](https://mlsanity.com/paper/2003.13848/) (2020)
- [LLMSecEval: A Dataset of Natural Language Prompts for Security Evaluations](https://mlsanity.com/paper/2303.09384/) (2023)
- [Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges](https://mlsanity.com/paper/2002.05442/) (2020)
- [CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models](https://mlsanity.com/paper/2302.04012/) (2023)
- [Using Large Language Models to Generate JUnit Tests: An Empirical Study](https://mlsanity.com/paper/2305.00418/) (2023)
- [CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences](https://mlsanity.com/paper/2202.06689/) (2022)
- [Fast and Memory-Efficient Neural Code Completion](https://mlsanity.com/paper/2004.13651/) (2020)

## Built on by (most cited first, 30 of 39)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges](https://mlsanity.com/paper/2510.23883/) (2025)
- [Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks](https://mlsanity.com/paper/2512.03262/) (2025)
- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [A Framework for Formalizing LLM Agent Security](https://mlsanity.com/paper/2603.19469/) (2026)
- [A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code](https://mlsanity.com/paper/2508.18106/) (2025)
- [Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers](https://mlsanity.com/paper/2403.15600/) (2024)
- [Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges](https://mlsanity.com/paper/2504.20799/) (2025)
- [From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security](https://mlsanity.com/paper/2412.15004/) (2024)
- [Rethinking the Evaluation of Secure Code Generation](https://mlsanity.com/paper/2503.15554/) (2025)
- [SandboxEval: Towards Securing Test Environment for Untrusted Code](https://mlsanity.com/paper/2504.00018/) (2025)
- [Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub](https://mlsanity.com/paper/2601.00477/) (2026)
- [How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study](https://mlsanity.com/paper/2412.18989/) (2024)
- [Code Hallucination](https://mlsanity.com/paper/2407.04831/) (2024)
- [How Safe Are AI-Generated Patches? A Large-scale Study on Security Risks in LLM and Agentic Automated Program Repair on SWE-bench](https://mlsanity.com/paper/2507.02976/) (2025)
- [Automating REST API Postman Test Cases Using LLM](https://mlsanity.com/paper/2404.10678/) (2024)
- [Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions](https://mlsanity.com/paper/2407.02028/) (2024)
- [Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning](https://mlsanity.com/paper/2502.13416/) (2025)
- [Enhancing Reliability in LLM-Based Secure Code Generation](https://mlsanity.com/paper/2605.24300/) (2026)
- [HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing](https://mlsanity.com/paper/2509.23835/) (2025)
- [LLM-CSEC: Empirical Evaluation of Security in C/C++ Code Generated by Large Language Models](https://mlsanity.com/paper/2511.18966/) (2025)
- [TOSSS: a CVE-based Software Security Benchmark for Large Language Models](https://mlsanity.com/paper/2603.10969/) (2026)
- [SEVRA-BENCH: Social Engineering of Vulnerabilities in Review Agents](https://mlsanity.com/paper/2606.13757/) (2026)
- [Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications](https://mlsanity.com/paper/2312.07069/) (2023)
- [Ocassionally Secure: A Comparative Analysis of Code Generation Assistants](https://mlsanity.com/paper/2402.00689/) (2024)
- [Designing Empirical Studies on LLM-Based Code Generation: Towards a Reference Framework](https://mlsanity.com/paper/2510.03862/) (2025)
- [Assessing the Software Security Comprehension of Large Language Models](https://mlsanity.com/paper/2512.21238/) (2025)
- [The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers](https://mlsanity.com/paper/2601.02045/) (2026)
- [Inference-Time Safety For Code LLMs Via Retrieval-Augmented Revision](https://mlsanity.com/paper/2603.01494/) (2026)
- [A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development](https://mlsanity.com/paper/2604.17763/) (2026)

## Abstract (arXiv)

With the growing popularity of Large Language Models (LLMs) in software engineers' daily practices, it is important to ensure that the code generated by these tools is not only functionally correct but also free of vulnerabilities. Although LLMs can help developers to be more productive, prior empirical studies have shown that LLMs can generate insecure code. There are two contributing factors to the insecure code generation. First, existing datasets used to evaluate LLMs do not adequately represent genuine software engineering tasks sensitive to security. Instead, they are often based on competitive programming challenges or classroom-type coding tasks. In real-world applications, the code produced is integrated into larger codebases, introducing potential security risks. Second, existing evaluation metrics primarily focus on the functional correctness of the generated code while ignoring security considerations. Therefore, in this paper, we described SALLM, a framework to benchmark LLMs' abilities to generate secure code systematically. This framework has three major components: a novel dataset of security-centric Python prompts, configurable assessment techniques to evaluate the generated code, and novel metrics to evaluate the models' performance from the perspective of secure code generation.