Salesforce Research Introduces INDICT: Enhancing AI-Generated Code Safety and Effectiveness

  • Salesforce Research introduces INDICT to improve safety and effectiveness of AI-generated code.
  • Challenges include potential vulnerabilities and malicious exploitation in generated code.
  • Current methods involve fine-tuning with safety datasets and rule-based detectors.
  • INDICT features a dual-critic system for comprehensive feedback and iterative refinement.
  • Operates through preemptive risk assessment and post-execution feedback stages.
  • Evaluation across diverse tasks and languages shows significant improvements in code quality.
  • INDICT enhances code safety by up to 30% and effectiveness metrics by up to 70%.

Main AI News:

Automating coding processes has the potential to revolutionize software development, streamlining operations and boosting efficiency. However, ensuring the security and reliability of AI-generated code poses significant challenges. Balancing functionality with safety is crucial, particularly given the potential for malicious exploitation.

In practical applications, language model systems (LLMs) often struggle with ambiguous or malicious instructions, inadvertently producing code that may contain vulnerabilities or facilitate attacks. Real-world studies underscore these risks; for instance, GitHub’s Copilot revealed that approximately 40% of generated programs harbor vulnerabilities. Mitigating these risks is paramount to fully leveraging LLMs in coding while ensuring safety.

Current approaches to mitigate these risks include fine-tuning LLMs with safety-focused datasets and implementing rule-based detectors to identify insecure code patterns. However, these methods have limitations; fine-tuning alone may not suffice against sophisticated attack prompts, and rule-based systems may overlook certain vulnerabilities.

Salesforce Research addresses these challenges with INDICT, a novel framework designed to enhance the safety and effectiveness of AI-generated code. INDICT employs a dual-critic system where one critic focuses on safety and the other on effectiveness. This framework facilitates iterative refinement of code outputs through comprehensive feedback loops. Critics leverage external knowledge sources such as code snippets, web searches, and code interpreters to provide informed critiques, ensuring robust evaluations.

The INDICT framework operates in two stages: preemptive and post-hoc feedback. During the preemptive stage, the safety-driven critic assesses potential risks associated with generated code, while the effectiveness-driven critic ensures alignment with task requirements. External knowledge sources augment these evaluations. The post-hoc stage reviews code post-execution, allowing critics to refine feedback based on observed outcomes. This approach enables proactive issue anticipation and continual model improvement.

Evaluation of INDICT across eight diverse tasks and programming languages using LLMs ranging from 7 billion to 70 billion parameters demonstrated significant enhancements in both safety and effectiveness metrics. The framework achieved a notable 10% absolute improvement in code quality across all tested models. For instance, in CyberSecEval-1, INDICT boosted code safety by up to 30%, with over 90% of outputs deemed secure. Effectiveness metrics also showed substantial gains, with INDICT-enhanced models surpassing state-of-the-art benchmarks by up to 70%.

INDICT’s success stems from its ability to provide contextually aware critiques, guiding LLMs to produce secure, functional code. By integrating safety and effectiveness feedback, this framework offers a robust solution to the challenges of AI-generated code, ensuring reliability in software development.


Salesforce’s INDICT framework represents a significant advancement in the realm of AI-generated code. By addressing critical challenges of security and functionality through its dual-critic approach and iterative feedback mechanisms, INDICT sets a new standard for reliability in software development. Its demonstrated improvements in code safety and effectiveness underscore its potential to enhance productivity and mitigate risks across the market.