--- title: "DeepInception vs IB4LLMs" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tmlr-group-deepinception-vs-zichuan-liu-ib4llms" tools: ["tmlr-group-deepinception", "zichuan-liu-ib4llms"] --- # DeepInception vs IB4LLMs *GraphCanon updated Aug 5, 2026* ## Verdict Pick DeepInception if deepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs; pick IB4LLMs if iB4LLMs (IBProtector) is an LLM jailbreak defense method using the Information Bottleneck principle to prevent adversarial prompts while preserving key information. [DeepInception](https://arxiv.org/pdf/2311.03191.pdf) reports 177 GitHub stars, 19 forks, and 0 open issues, last pushed Feb 20, 2024. [IB4LLMs](https://zichuan-liu.github.io/projects/IBProtector/index.html) has 25 stars, 2 forks, and 4 open issues, last pushed Nov 7, 2024. Figures are from public GitHub metadata via [DeepInception's repository](https://github.com/tmlr-group/DeepInception) and [IB4LLMs's repository](https://github.com/zichuan-liu/IB4LLMs). | | [DeepInception](/tools/tmlr-group-deepinception.md) | [IB4LLMs](/tools/zichuan-liu-ib4llms.md) | | --- | --- | --- | | Tagline | Develops techniques to influence large language model behavior | Protecting Your LLMs with Information Bottleneck | | Stars | 177 | 25 | | Forks | 19 | 2 | | Open issues | 0 | 4 | | Language | Python | Python | | Adopt for | DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs. | IB4LLMs (IBProtector) is an LLM jailbreak defense method using the Information Bottleneck principle to prevent adversarial prompts while preserving key information. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepInception](/tools/tmlr-group-deepinception.md) | [IB4LLMs](/tools/zichuan-liu-ib4llms.md) | | --- | --- | --- | | Days since push | 896d | 635d | | Open issues (now) | 0 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/tmlr-group-deepinception/trust.md) | [trust report](/tools/zichuan-liu-ib4llms/trust.md) | ## Shared compatibility - **Python**: [DeepInception](/tools/tmlr-group-deepinception.md) - Python runtime; [IB4LLMs](/tools/zichuan-liu-ib4llms.md) - Python runtime ## 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. ## Decision facts: IB4LLMs - **Pricing:** unknown - License information unavailable; specific model pricing not provided. - **Requirements:** Dependencies include datasets==2.14.5, torch==2.1.1, transformers==4.40.1 among others. - **Adopt for:** IB4LLMs (IBProtector) is an LLM jailbreak defense method using the Information Bottleneck principle to prevent adversarial prompts while preserving key information. ## Choose when ### 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 ### Choose IB4LLMs if… - Pricing: License information unavailable; specific model pricing not provided.. - Requirements: Dependencies include datasets==2.14.5, torch==2.1.1, transformers==4.40.1 among others.. - Tags unique to IB4LLMs: evaluation scripts, finetuning, inference, information bottleneck. - Also covers Evaluation & Observability. - When you need a specialized tool for guarding against jailbreaks in your language models without losing important data. ## 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 ## When NOT to use IB4LLMs - If you require a more generalized model protection approach that does not rely strictly on the Information Bottleneck principle. - When your environment lacks support for specific packages like `fschat==0.2.20` which is crucial and cannot be updated due to potential conflicts. ## Common questions ### What is the difference between DeepInception and IB4LLMs? DeepInception: Develops techniques to influence large language model behavior. IB4LLMs: Protecting Your LLMs with Information Bottleneck. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepInception over IB4LLMs? Choose DeepInception over IB4LLMs 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 choose IB4LLMs over DeepInception? Choose IB4LLMs over DeepInception when Pricing: License information unavailable; specific model pricing not provided.; Requirements: Dependencies include datasets==2.14.5, torch==2.1.1, transformers==4.40.1 among others.; Tags unique to IB4LLMs: evaluation scripts, finetuning, inference, information bottleneck; Also covers Evaluation & Observability; When you need a specialized tool for guarding against jailbreaks in your language models without losing important data. ### 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 ### When should I avoid IB4LLMs? If you require a more generalized model protection approach that does not rely strictly on the Information Bottleneck principle. When your environment lacks support for specific packages like `fschat==0.2.20` which is crucial and cannot be updated due to potential conflicts. ### Is DeepInception or IB4LLMs more popular on GitHub? DeepInception has more GitHub stars (177 vs 25). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepInception and IB4LLMs open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DeepInception or IB4LLMs? GraphCanon lists graph-backed alternatives at [DeepInception alternatives](/tools/tmlr-group-deepinception/alternatives) and [IB4LLMs alternatives](/tools/zichuan-liu-ib4llms/alternatives) ([DeepInception markdown twin](/tools/tmlr-group-deepinception/alternatives.md), [IB4LLMs markdown twin](/tools/zichuan-liu-ib4llms/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/tmlr-group-deepinception-vs-zichuan-liu-ib4llms.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepInception or IB4LLMs? DeepInception: Dormant. IB4LLMs: 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 DeepInception and IB4LLMs? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepInception trust report](/tools/tmlr-group-deepinception/trust); [IB4LLMs trust report](/tools/zichuan-liu-ib4llms/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tmlr-group-deepinception`](/api/graphcanon/graph?tool=tmlr-group-deepinception) - 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/_