--- title: "GPTFuzz vs DeepInception" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/sherdencooper-gptfuzz-vs-tmlr-group-deepinception" tools: ["sherdencooper-gptfuzz", "tmlr-group-deepinception"] --- # GPTFuzz vs DeepInception *GraphCanon updated Aug 5, 2026* ## Verdict Pick GPTFuzz if gPTFuzz leverages auto-generated jailbreak prompts to red team large language models for testing and evaluation; pick DeepInception if deepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs. [GPTFuzz](https://github.com/sherdencooper/GPTFuzz) reports 604 GitHub stars, 87 forks, and 17 open issues, last pushed Feb 27, 2026. [DeepInception](https://arxiv.org/pdf/2311.03191.pdf) has 177 stars, 19 forks, and 0 open issues, last pushed Feb 20, 2024. Figures are from public GitHub metadata via [GPTFuzz's repository](https://github.com/sherdencooper/GPTFuzz) and [DeepInception's repository](https://github.com/tmlr-group/DeepInception). | | [GPTFuzz](/tools/sherdencooper-gptfuzz.md) | [DeepInception](/tools/tmlr-group-deepinception.md) | | --- | --- | --- | | Tagline | Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts | Develops techniques to influence large language model behavior | | Stars | 604 | 177 | | Forks | 87 | 19 | | Open issues | 17 | 0 | | Language | Python | Python | | Adopt for | GPTFuzz leverages auto-generated jailbreak prompts to red team large language models for testing and evaluation. | DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Evaluation & Observability, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [GPTFuzz](/tools/sherdencooper-gptfuzz.md) | [DeepInception](/tools/tmlr-group-deepinception.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 158d | 896d | | Open issues (now) | 17 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/sherdencooper-gptfuzz/trust.md) | [trust report](/tools/tmlr-group-deepinception/trust.md) | ## Decision facts: GPTFuzz - **Adopt for:** GPTFuzz leverages auto-generated jailbreak prompts to red team large language models for testing and evaluation. ## Decision facts: DeepInception - **Pricing:** freemium - The tool is free under the MIT license. However, using it may incur costs from third-party services like OpenAI API keys for accessing closed-source models - **Requirements:** Requires PyTorch ≥1.10 with GPU support; Environment modification needed to include path configurations for Vicuna, Llama-2, and Falcon - **Adopt for:** DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs. ## Choose when ### Choose GPTFuzz if… - Tags unique to GPTFuzz: jailbreak prompts, large language models, red-teaming. - Also covers Evaluation & Observability. - When you need to test the robustness of LLMs against potential manipulative input designed to bypass content controls. ### Choose DeepInception if… - Pricing: The tool is free under the MIT license. However, using it may incur costs from third-party services like OpenAI API keys for accessing closed-source models. - Requirements: Requires PyTorch ≥1.10 with GPU support; Environment modification needed to include path configurations for Vicuna, Llama-2, and Falcon. - Tags unique to DeepInception: deep, gpt, inception, jailbreak. - When you need to research the effects of specific modifications on the safety and trustworthiness of GPT-3, GPT-4, Vicuna, Llama-2, or Falcon models ## When NOT to use GPTFuzz - If your project requires straightforward, uncontroversial testing tools that do not engage with sensitive content control evasion techniques. - For general-purpose debugging and optimization tasks where red teaming tactics are not necessary or appropriate. ## When NOT to use DeepInception - For deployment in production environments where strict adherence to ethical and regulatory guidelines is mandatory, due to the experimental nature of DeepInception - When there's a need for direct application without exploring modification effects, as DeepInception requires setting up an environment that supports specific models and modifications ## Common questions ### What is the difference between GPTFuzz and DeepInception? GPTFuzz: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts. DeepInception: Develops techniques to influence large language model behavior. See the comparison table for live GitHub stats and shared categories. ### When should I choose GPTFuzz over DeepInception? Choose GPTFuzz over DeepInception when Tags unique to GPTFuzz: jailbreak prompts, large language models, red-teaming; Also covers Evaluation & Observability; When you need to test the robustness of LLMs against potential manipulative input designed to bypass content controls. ### When should I choose DeepInception over GPTFuzz? Choose DeepInception over GPTFuzz when Pricing: The tool is free under the MIT license. However, using it may incur costs from third-party services like OpenAI API keys for accessing closed-source models; Requirements: Requires PyTorch ≥1.10 with GPU support; Environment modification needed to include path configurations for Vicuna, Llama-2, and Falcon; Tags unique to DeepInception: deep, gpt, inception, jailbreak; When you need to research the effects of specific modifications on the safety and trustworthiness of GPT-3, GPT-4, Vicuna, Llama-2, or Falcon models. ### When should I avoid GPTFuzz? If your project requires straightforward, uncontroversial testing tools that do not engage with sensitive content control evasion techniques. For general-purpose debugging and optimization tasks where red teaming tactics are not necessary or appropriate. ### When should I avoid DeepInception? For deployment in production environments where strict adherence to ethical and regulatory guidelines is mandatory, due to the experimental nature of DeepInception When there's a need for direct application without exploring modification effects, as DeepInception requires setting up an environment that supports specific models and modifications ### Is GPTFuzz or DeepInception more popular on GitHub? GPTFuzz has more GitHub stars (604 vs 177). Stars measure visibility, not whether either tool fits your constraints. ### Are GPTFuzz and DeepInception open source? Yes - both are open-source projects on GitHub (GPTFuzz: MIT, DeepInception: MIT). ### Where can I find alternatives to GPTFuzz or DeepInception? GraphCanon lists graph-backed alternatives at [GPTFuzz alternatives](/tools/sherdencooper-gptfuzz/alternatives) and [DeepInception alternatives](/tools/tmlr-group-deepinception/alternatives) ([GPTFuzz markdown twin](/tools/sherdencooper-gptfuzz/alternatives.md), [DeepInception markdown twin](/tools/tmlr-group-deepinception/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/sherdencooper-gptfuzz-vs-tmlr-group-deepinception.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, GPTFuzz or DeepInception? GPTFuzz: Slowing. DeepInception: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for GPTFuzz and DeepInception? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [GPTFuzz trust report](/tools/sherdencooper-gptfuzz/trust); [DeepInception trust report](/tools/tmlr-group-deepinception/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=sherdencooper-gptfuzz`](/api/graphcanon/graph?tool=sherdencooper-gptfuzz) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_