Comparison
GPTFuzz vs DeepInception
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.
Markdown twin · GPTFuzz alternatives · DeepInception alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | GPTFuzz | DeepInception |
|---|---|---|
| Maintenance | Slowing (158d since push) As of 2w · github_public_v1 | Dormant (896d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- GPTFuzz
- Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts
- DeepInception
- Develops techniques to influence large language model behavior
Stars
- GPTFuzz
- 604
- DeepInception
- 177
Forks
- GPTFuzz
- 87
- DeepInception
- 19
Open issues
- GPTFuzz
- 17
- DeepInception
- 0
Language
- GPTFuzz
- Python
- DeepInception
- Python
Adopt for
- GPTFuzz
- GPTFuzz leverages auto-generated jailbreak prompts to red team large language models for testing and evaluation.
- DeepInception
- DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs.
Persona
- GPTFuzz
- -
- DeepInception
- -
Runtime
- GPTFuzz
- -
- DeepInception
- -
License
- GPTFuzz
- MIT
- DeepInception
- MIT
Last pushed
- GPTFuzz
- Feb 27, 2026
- DeepInception
- Feb 20, 2024
Categories
- GPTFuzz
- Evaluation & Observability, LLM Frameworks
- DeepInception
- LLM Frameworks
Trust and health
Maintenance
- GPTFuzz
- Slowing (36%)
- DeepInception
- Dormant (18%)
Days since push
- GPTFuzz
- 158d
- DeepInception
- 896d
Open issues (now)
- GPTFuzz
- 17
- DeepInception
- 0
Owner type
- GPTFuzz
- User
- DeepInception
- Organization
OSV dependency advisories
- GPTFuzz
- No lockfile (source not queried)
- DeepInception
- Published findings
Full report
- GPTFuzz
- Trust report
- DeepInception
- Trust report
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.
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (sherdencooper/GPTFuzz) · observed Aug 5, 2026
- GitHub forks (sherdencooper/GPTFuzz) · observed Aug 5, 2026
- Last push (sherdencooper/GPTFuzz) · observed Feb 27, 2026
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tmlr-group/DeepInception) · observed Aug 5, 2026
- GitHub forks (tmlr-group/DeepInception) · observed Aug 5, 2026
- Last push (tmlr-group/DeepInception) · observed Feb 20, 2024
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: GPTFuzz 604 · DeepInception 177 (synced Aug 5, 2026).
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 and DeepInception alternatives (GPTFuzz markdown twin, DeepInception markdown twin), 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 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; DeepInception trust report.