---
title: "Confidence_Elicitation_Attacks vs chatgpt-plugin-eval"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/aniloid2-confidence-elicitation-attacks-vs-llm-platform-security-chatgpt-plugin-eval"
tools: ["aniloid2-confidence-elicitation-attacks", "llm-platform-security-chatgpt-plugin-eval"]
---

# Confidence_Elicitation_Attacks vs chatgpt-plugin-eval

*GraphCanon updated Aug 5, 2026*

## Verdict

Pick Confidence_Elicitation_Attacks if explores new attack vectors on large language models by eliciting confidence; pick chatgpt-plugin-eval if chatgpt-plugin-eval is an evaluation framework designed specifically to assess security, privacy, and safety concerns related to third-party plugins interfacing with large language models like ChatGPT.

[Confidence_Elicitation_Attacks](https://github.com/Aniloid2/Confidence_Elicitation_Attacks) reports 6 GitHub stars, 0 forks, and 1 open issues, last pushed Mar 4, 2025. [chatgpt-plugin-eval](https://llm-platform-security.github.io/chatgpt-plugin-eval/) has 29 stars, 7 forks, and 1 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [Confidence_Elicitation_Attacks's repository](https://github.com/Aniloid2/Confidence_Elicitation_Attacks) and [chatgpt-plugin-eval's repository](https://github.com/llm-platform-security/chatgpt-plugin-eval).

| | [Confidence_Elicitation_Attacks](/tools/aniloid2-confidence-elicitation-attacks.md) | [chatgpt-plugin-eval](/tools/llm-platform-security-chatgpt-plugin-eval.md) |
| --- | --- | --- |
| Tagline | Confidence Elicitation Attacks on Large Language Models | Framework for Evaluating Security in LLM Plugin Ecosystems |
| Stars | 6 | 29 |
| Forks | 0 | 7 |
| Open issues | 1 | 1 |
| Language | Python | HTML |
| Adopt for | Explores new attack vectors on large language models by eliciting confidence. | chatgpt-plugin-eval is an evaluation framework designed specifically to assess security, privacy, and safety concerns related to third-party plugins interfacing with large language models like ChatGPT. |
| Persona | - | - |
| Runtime | - | - |
| License | (unknown) | The license information for chatgpt-plugin-eval is unknown. |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Confidence_Elicitation_Attacks](/tools/aniloid2-confidence-elicitation-attacks.md) | [chatgpt-plugin-eval](/tools/llm-platform-security-chatgpt-plugin-eval.md) |
| --- | --- | --- |
| Days since push | 518d | 736d |
| Owner type | User | Organization |
| Full report | [trust report](/tools/aniloid2-confidence-elicitation-attacks/trust.md) | [trust report](/tools/llm-platform-security-chatgpt-plugin-eval/trust.md) |

## Decision facts: Confidence_Elicitation_Attacks

- **Hosting:** unknown - Research paper outlines attack methods for large language models via confidence elicitation.
- **Adopt for:** Explores new attack vectors on large language models by eliciting confidence.
- **License detail:** (unknown)
- **Runtime:** unknown

## Decision facts: chatgpt-plugin-eval

- **Pricing:** freemium
- **Adopt for:** chatgpt-plugin-eval is an evaluation framework designed specifically to assess security, privacy, and safety concerns related to third-party plugins interfacing with large language models like ChatGPT.
- **License detail:** The license information for chatgpt-plugin-eval is unknown.

## Choose when

### Choose Confidence_Elicitation_Attacks if…

- Confidence_Elicitation_Attacks is primarily Python; chatgpt-plugin-eval is HTML.
- Research paper outlines attack methods for large language models via confidence elicitation.
- Tags unique to Confidence_Elicitation_Attacks: attack vectors, confidence analysis, llm security, model evaluation.
- When studying adversarial attacks specifically targeting large language models

### Choose chatgpt-plugin-eval if…

- chatgpt-plugin-eval is primarily HTML; Confidence_Elicitation_Attacks is Python.
- Tags unique to chatgpt-plugin-eval: chatgpt, llm-plugins, privacy, security.
- - When evaluating the security risks of integrating third-party services into your LLM platform through plugins

## When NOT to use Confidence_Elicitation_Attacks

- For general debugging of machine learning models outside of adversarial contexts
- In scenarios focused on improving the performance rather than exposing security flaws

## When NOT to use chatgpt-plugin-eval

- - In cases where only generic, high-level security guidance is required without an in-depth framework analysis
- - When the primary focus is on improving performance metrics rather than addressing specific security and privacy concerns of LLM plugins

## Common questions

### What is the difference between Confidence_Elicitation_Attacks and chatgpt-plugin-eval?

Confidence_Elicitation_Attacks: Confidence Elicitation Attacks on Large Language Models. chatgpt-plugin-eval: Framework for Evaluating Security in LLM Plugin Ecosystems. See the comparison table for live GitHub stats and shared categories.

### When should I choose Confidence_Elicitation_Attacks over chatgpt-plugin-eval?

Choose Confidence_Elicitation_Attacks over chatgpt-plugin-eval when Confidence_Elicitation_Attacks is primarily Python; chatgpt-plugin-eval is HTML; Research paper outlines attack methods for large language models via confidence elicitation; Tags unique to Confidence_Elicitation_Attacks: attack vectors, confidence analysis, llm security, model evaluation; When studying adversarial attacks specifically targeting large language models.

### When should I choose chatgpt-plugin-eval over Confidence_Elicitation_Attacks?

Choose chatgpt-plugin-eval over Confidence_Elicitation_Attacks when chatgpt-plugin-eval is primarily HTML; Confidence_Elicitation_Attacks is Python; Tags unique to chatgpt-plugin-eval: chatgpt, llm-plugins, privacy, security; - When evaluating the security risks of integrating third-party services into your LLM platform through plugins.

### When should I avoid Confidence_Elicitation_Attacks?

For general debugging of machine learning models outside of adversarial contexts In scenarios focused on improving the performance rather than exposing security flaws

### When should I avoid chatgpt-plugin-eval?

- In cases where only generic, high-level security guidance is required without an in-depth framework analysis - When the primary focus is on improving performance metrics rather than addressing specific security and privacy concerns of LLM plugins

### Is Confidence_Elicitation_Attacks or chatgpt-plugin-eval more popular on GitHub?

chatgpt-plugin-eval has more GitHub stars (29 vs 6). Stars measure visibility, not whether either tool fits your constraints.

### Are Confidence_Elicitation_Attacks and chatgpt-plugin-eval open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to Confidence_Elicitation_Attacks or chatgpt-plugin-eval?

GraphCanon lists graph-backed alternatives at [Confidence_Elicitation_Attacks alternatives](/tools/aniloid2-confidence-elicitation-attacks/alternatives) and [chatgpt-plugin-eval alternatives](/tools/llm-platform-security-chatgpt-plugin-eval/alternatives) ([Confidence_Elicitation_Attacks markdown twin](/tools/aniloid2-confidence-elicitation-attacks/alternatives.md), [chatgpt-plugin-eval markdown twin](/tools/llm-platform-security-chatgpt-plugin-eval/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/aniloid2-confidence-elicitation-attacks-vs-llm-platform-security-chatgpt-plugin-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Confidence_Elicitation_Attacks or chatgpt-plugin-eval?

Confidence_Elicitation_Attacks: Dormant. chatgpt-plugin-eval: 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 Confidence_Elicitation_Attacks and chatgpt-plugin-eval?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Confidence_Elicitation_Attacks trust report](/tools/aniloid2-confidence-elicitation-attacks/trust); [chatgpt-plugin-eval trust report](/tools/llm-platform-security-chatgpt-plugin-eval/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=aniloid2-confidence-elicitation-attacks`](/api/graphcanon/graph?tool=aniloid2-confidence-elicitation-attacks)
- 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/_