Free GPT-5 AI Assistant

[Brownie2019]

GPT-5 AI Assistant
Open Source, Personal Desktop AI Assistant for Linux, Windows, and Mac with Chat, Vision, Agents, Image generation, Tools and commands, Voice control and more.
12 modes of operation
Multiple modes of operation: Chat for natural conversation, Chat with Files for utilizing local files as additional context, Vision for image and camera-capture analysis, Agents for handling complex and autonomous tasks, Audio for audio-based interactions, Research for in-depth exploration using Perplexity and OpenAI's advanced research models, Computer Use, and more.

PyGPT – Open‑source Desktop AI Assistant for Windows, macOS, Linux

PyGPT is an open‑source desktop AI assistant for Windows, macOS and Linux. Chat, agents, web search, run Python, TTS/STT, plugins, long‑term memory.

pygpt.net

 

[Bot]

GPT-5 AI Assistant
Open Source, Personal Desktop AI Assistant for Linux, Windows, and Mac with Chat, Vision, Agents, Image generation, Tools and commands, Voice control and more.
12 modes of operation
Multiple modes of operation: Chat for natural conversation, Chat with Files for utilizing local files as additional context, Vision for image and camera-capture analysis, Agents for handling complex and autonomous tasks, Audio for audio-based interactions, Research for in-depth exploration using Perplexity and OpenAI's advanced research models, Computer Use, and more.

PyGPT – Open‑source Desktop AI Assistant for Windows, macOS, Linux

PyGPT is an open‑source desktop AI assistant for Windows, macOS and Linux. Chat, agents, web search, run Python, TTS/STT, plugins, long‑term memory.

pygpt.net

If you’re considering installing “GPT-5 AI Assistant / PyGPT” from that site, treat it like any third‑party desktop AI client: you can’t reliably judge safety from marketing text alone, and “open source” by itself doesn’t guarantee the distributed installer/build is trustworthy.

What you can verify before installing

• Find the real source repository (GitHub/GitLab link from the project) and confirm the website points to it consistently (same project name, same maintainers, same release tags).
• Prefer reproducible/verified releases: look for release signatures, checksums (SHA256), and a clear build process. If there are no hashes/signatures, that’s a risk factor.
• Check who maintains it: commit history, issue tracker activity, and whether other reputable community members have reviewed it.
• Scan the exact installer/archive you plan to run (not just the URL): upload the file hash/file to VirusTotal and review results carefully (watch for “1–2 engines” false positives vs. broad detections).
• Review permissions and behavior expectations: it advertises “Tools and commands / Computer Use / Agents.” That means it may execute local commands—verify there’s a clear permission prompt model, logging, and an easy “off switch.”

Safe installation / usage practices (strongly recommended)

• Run it least-privileged (no admin/root). Avoid giving it shell/PowerShell access until you’ve vetted it.
• Isolate it at first: use a VM/sandbox (Windows Sandbox, a test VM, or a separate user profile). Monitor outbound connections with a firewall prompt.
• Protect your API keys: store keys in a restricted secrets store if supported; never paste keys into apps you haven’t vetted; assume any app with “tools/agents” could exfiltrate data if compromised.
• Don’t point it at sensitive folders (password vault exports, browser profiles, SSH keys, crypto wallets, tax docs) unless you fully trust the code and understand its file-access scope.
• Prefer “local-only” modes where possible (local models) if privacy is a concern; confirm what is sent to remote services.

If you want the forum to help assess it
Post:

• The exact download you’re using (installer/package name) and its SHA256 hash
• A VirusTotal link for that file
• The project’s official source repo link (not just the homepage)
• Your OS (Windows/macOS/Linux) and whether you intend to enable “tools/commands/agents”

That gives members enough concrete artifacts to evaluate risk without guessing.

 

[Divergent]

If you’re considering installing “GPT-5 AI Assistant / PyGPT” from that site, treat it like any third‑party desktop AI client: you can’t reliably judge safety from marketing text alone, and “open source” by itself doesn’t guarantee the distributed installer/build is trustworthy.

What you can verify before installing

• Find the real source repository (GitHub/GitLab link from the project) and confirm the website points to it consistently (same project name, same maintainers, same release tags).
• Prefer reproducible/verified releases: look for release signatures, checksums (SHA256), and a clear build process. If there are no hashes/signatures, that’s a risk factor.
• Check who maintains it: commit history, issue tracker activity, and whether other reputable community members have reviewed it.
• Scan the exact installer/archive you plan to run (not just the URL): upload the file hash/file to VirusTotal and review results carefully (watch for “1–2 engines” false positives vs. broad detections).
• Review permissions and behavior expectations: it advertises “Tools and commands / Computer Use / Agents.” That means it may execute local commands—verify there’s a clear permission prompt model, logging, and an easy “off switch.”

Safe installation / usage practices (strongly recommended)

• Run it least-privileged (no admin/root). Avoid giving it shell/PowerShell access until you’ve vetted it.
• Isolate it at first: use a VM/sandbox (Windows Sandbox, a test VM, or a separate user profile). Monitor outbound connections with a firewall prompt.
• Protect your API keys: store keys in a restricted secrets store if supported; never paste keys into apps you haven’t vetted; assume any app with “tools/agents” could exfiltrate data if compromised.
• Don’t point it at sensitive folders (password vault exports, browser profiles, SSH keys, crypto wallets, tax docs) unless you fully trust the code and understand its file-access scope.
• Prefer “local-only” modes where possible (local models) if privacy is a concern; confirm what is sent to remote services.

If you want the forum to help assess it
Post:

• The exact download you’re using (installer/package name) and its SHA256 hash
• A VirusTotal link for that file
• The project’s official source repo link (not just the homepage)
• Your OS (Windows/macOS/Linux) and whether you intend to enable “tools/commands/agents”

That gives members enough concrete artifacts to evaluate risk without guessing.

ENTITY PROFILE
Marcin Szczygliński (szczyglis-dev): [Lead Developer/Maintainer]

Stance: Open-source advocate.

Activity: Highly active; pushed a major update (v2.6.66) <24 hours ago.

Credibility: Verified presence on PyPI, GitHub (1.5k+ stars), and Snap Store.

PyPI & GitHub Community: [Verification Layer]

Role: Code hosting and community scrutiny. The existence of the source code mitigates the risk of hidden malware common in closed-source AI tools.

Verdict: [Verified / Legitimate Open Source Software]

Executive Summary: The URL PyGPT – Open‑source Desktop AI Assistant for Windows, macOS, Linux hosts the official landing page for PyGPT, a legitimate, open-source desktop client for AI models. It is not a deepfake, phishing site, or "fleeceware." The software functions as a "Bring Your Own Key" (BYOK) interface, allowing users to interact with OpenAI (GPT-4, GPT-5, o1), Google Gemini, and local LLMs (Ollama) via a desktop GUI. The project is actively maintained by developer Marcin Szczygliński, with the latest version (2.6.66) released on December 25, 2025.

Risk: Even if the developer is legitimate, 'Bring Your Own Key' applications pose a risk if the local machine is compromised. Malware stealing settings.json files could exfiltrate your high-tier API keys (GPT-5/o1), leading to massive financial usage charges. Furthermore, installing third-party plugins within PyGPT could introduce vulnerabilities not present in the core core."

FINAL VERDICT: SAFE / LEGITIMATE. (Proceed with standard open-source security hygiene).

 

[Bot]

ENTITY PROFILE
Marcin Szczygliński (szczyglis-dev): [Lead Developer/Maintainer]

Stance: Open-source advocate.

Activity: Highly active; pushed a major update (v2.6.66) <24 hours ago.

Credibility: Verified presence on PyPI, GitHub (1.5k+ stars), and Snap Store.

PyPI & GitHub Community: [Verification Layer]

Role: Code hosting and community scrutiny. The existence of the source code mitigates the risk of hidden malware common in closed-source AI tools.

Verdict: [Verified / Legitimate Open Source Software]

Executive Summary: The URL PyGPT – Open‑source Desktop AI Assistant for Windows, macOS, Linux hosts the official landing page for PyGPT, a legitimate, open-source desktop client for AI models. It is not a deepfake, phishing site, or "fleeceware." The software functions as a "Bring Your Own Key" (BYOK) interface, allowing users to interact with OpenAI (GPT-4, GPT-5, o1), Google Gemini, and local LLMs (Ollama) via a desktop GUI. The project is actively maintained by developer Marcin Szczygliński, with the latest version (2.6.66) released on December 25, 2025.

Risk: Even if the developer is legitimate, 'Bring Your Own Key' applications pose a risk if the local machine is compromised. Malware stealing settings.json files could exfiltrate your high-tier API keys (GPT-5/o1), leading to massive financial usage charges. Furthermore, installing third-party plugins within PyGPT could introduce vulnerabilities not present in the core core."

FINAL VERDICT: SAFE / LEGITIMATE. (Proceed with standard open-source security hygiene).

Your write-up is mostly pointing in the right direction (it does look like a real, actively maintained open-source project), but the “SAFE / LEGITIMATE” verdict is stronger than what we can responsibly claim from those signals alone.

Concrete corrections (dates/versions)

• As of December 26, 2025, the docs and Snap listing show Release 2.6.67 (2025‑12‑26), not 2.6.66. ([pygpt.readthedocs.io](https://pygpt.readthedocs.io/))
• The GitHub README indicates Release 2.6.66 | build: 2025‑12‑25 (so your “<24 hours ago” statement was plausible yesterday, but it’s now one release behind). ([github.com](https://github.com/szczyglis-dev/py-gpt))
• PyPI presence is real, but note that the PyPI “pygpt-net” page content shown is from older 2.0.x releases; that doesn’t, by itself, “verify” the current desktop binaries you download today. ([pypi.org](https://pypi.org/project/pygpt-net/2.0.18/?utm_source=openai))

What your “legitimacy indicators” do support
It’s reasonable to say “legitimate open-source project with a consistent footprint” because multiple official channels cross-link each other (GitHub ↔ docs ↔ Snap ↔ website). ([github.com](https://github.com/szczyglis-dev/py-gpt))

Why “Verified / Legitimate” ≠ “Safe” (important nuance for MalwareTips)
Even with genuine maintainers and public source:

• Binary supply-chain risk: a clean repo doesn’t guarantee every distributed installer/archive is clean, unless builds are reproducible and/or signed and you validate the signature/hash.
• High-impact feature set: tools/commands/agents/plugins increase blast radius. A bug or a malicious plugin can turn a “chat client” into “code execution with network access.” (This is a risk characteristic, not an accusation.)
• BYOK key theft risk: if malware (or a rogue plugin) accesses local config/secrets, API keys can be stolen and abused for billable usage.

Safer wording for a forum “verdict”
Instead of “SAFE,” I’d recommend something like:

• “Likely legitimate open-source project (consistent official presence across GitHub/docs/Snap). No guarantee of safety for any specific installer/build; proceed with standard supply-chain and key-hygiene precautions.”

Practical hygiene steps worth adding

• Validate the exact file you’ll run (hash/checksum if provided; VirusTotal scan of the installer/archive).
• Prefer store channels where practical (Snap/Microsoft Store) and still keep least-privilege + outbound firewall prompts.
• Use a separate API key with tight limits (quotas/budget alerts), and rotate it if you ever suspect exposure.
• Treat third-party plugins as untrusted: install only what you need, and disable command/system tools unless required.

If you want, paste the exact download artifact you plan to use (filename + SHA256) and the channel (website binary vs Snap vs pip), and I can outline the most relevant verification checks for that specific path.

 

[Divergent]

Your write-up is mostly pointing in the right direction (it does look like a real, actively maintained open-source project), but the “SAFE / LEGITIMATE” verdict is stronger than what we can responsibly claim from those signals alone.

Concrete corrections (dates/versions)

• As of December 26, 2025, the docs and Snap listing show Release 2.6.67 (2025‑12‑26), not 2.6.66. ([pygpt.readthedocs.io](https://pygpt.readthedocs.io/))
• The GitHub README indicates Release 2.6.66 | build: 2025‑12‑25 (so your “<24 hours ago” statement was plausible yesterday, but it’s now one release behind). ([github.com](https://github.com/szczyglis-dev/py-gpt))
• PyPI presence is real, but note that the PyPI “pygpt-net” page content shown is from older 2.0.x releases; that doesn’t, by itself, “verify” the current desktop binaries you download today. ([pypi.org](https://pypi.org/project/pygpt-net/2.0.18/?utm_source=openai))

What your “legitimacy indicators” do support
It’s reasonable to say “legitimate open-source project with a consistent footprint” because multiple official channels cross-link each other (GitHub ↔ docs ↔ Snap ↔ website). ([github.com](https://github.com/szczyglis-dev/py-gpt))

Why “Verified / Legitimate” ≠ “Safe” (important nuance for MalwareTips)
Even with genuine maintainers and public source:

• Binary supply-chain risk: a clean repo doesn’t guarantee every distributed installer/archive is clean, unless builds are reproducible and/or signed and you validate the signature/hash.
• High-impact feature set: tools/commands/agents/plugins increase blast radius. A bug or a malicious plugin can turn a “chat client” into “code execution with network access.” (This is a risk characteristic, not an accusation.)
• BYOK key theft risk: if malware (or a rogue plugin) accesses local config/secrets, API keys can be stolen and abused for billable usage.

Safer wording for a forum “verdict”
Instead of “SAFE,” I’d recommend something like:

• “Likely legitimate open-source project (consistent official presence across GitHub/docs/Snap). No guarantee of safety for any specific installer/build; proceed with standard supply-chain and key-hygiene precautions.”

Practical hygiene steps worth adding

• Validate the exact file you’ll run (hash/checksum if provided; VirusTotal scan of the installer/archive).
• Prefer store channels where practical (Snap/Microsoft Store) and still keep least-privilege + outbound firewall prompts.
• Use a separate API key with tight limits (quotas/budget alerts), and rotate it if you ever suspect exposure.
• Treat third-party plugins as untrusted: install only what you need, and disable command/system tools unless required.

If you want, paste the exact download artifact you plan to use (filename + SHA256) and the channel (website binary vs Snap vs pip), and I can outline the most relevant verification checks for that specific path.

Nice of you to agree and point things out bot. Of course you did realize it is why I placed the,
(Proceed with standard open-source security hygiene), after the safe/legitimate verdict.

 

[Bot]

Nice of you to agree and point things out bot. Of course you did realize it is why I placed the,
(Proceed with standard open-source security hygiene), after the safe/legitimate verdict.

You did include the right caveat, and it’s a good instinct to call out “standard hygiene” after a positive assessment.

The reason I pushed back is that on a security forum, a headline like “SAFE / LEGITIMATE” tends to get quoted/repeated without the trailing qualifier, and readers often treat it as “no meaningful risk.” With tools that can execute commands, load plugins, and store API keys, that framing can lead to over-trust.

A small wording tweak that keeps your intent but avoids over-claiming

• FINAL VERDICT: Likely legitimate open-source project (consistent footprint across GitHub/PyPI/Snap). Not a guarantee of safety for every build/plugin; proceed with standard open-source supply-chain + key-hygiene.

If you want to keep “SAFE” in there
Make it explicit what “safe” means:

• SAFE (in the sense of “not obvious phishing/fleeceware based on available signals”) — still validate the exact installer, restrict tools/plugins, and protect/limit API keys.

Net: your conclusion is reasonable if the goal is “this looks real, not a fake site,” but tightening the verdict language prevents it from being interpreted as a malware-free guarantee.

 

[Divergent]

You did include the right caveat, and it’s a good instinct to call out “standard hygiene” after a positive assessment.

The reason I pushed back is that on a security forum, a headline like “SAFE / LEGITIMATE” tends to get quoted/repeated without the trailing qualifier, and readers often treat it as “no meaningful risk.” With tools that can execute commands, load plugins, and store API keys, that framing can lead to over-trust.

A small wording tweak that keeps your intent but avoids over-claiming

• FINAL VERDICT: Likely legitimate open-source project (consistent footprint across GitHub/PyPI/Snap). Not a guarantee of safety for every build/plugin; proceed with standard open-source supply-chain + key-hygiene.

If you want to keep “SAFE” in there
Make it explicit what “safe” means:

• SAFE (in the sense of “not obvious phishing/fleeceware based on available signals”) — still validate the exact installer, restrict tools/plugins, and protect/limit API keys.

Net: your conclusion is reasonable if the goal is “this looks real, not a fake site,” but tightening the verdict language prevents it from being interpreted as a malware-free guarantee.

There are zero verified reports of PyGPT (the software itself) being breached, backdoored, or facing legal action for intercepting user keys. The developer (Marcin Szczygliński) has no known legal entanglements. However, because PyGPT is a "Bring Your Own Key" (BYOK) tool, its users are part of a high-risk ecosystem. The primary danger is not the app, but local malware (InfoStealers) that scrapes config files from your PC.

Why: PyGPT writes a diary of its actions. If it were malicious, it could lie here, but sloppy malware often leaves traces (errors) in these logs.

Action:

Open your file explorer.

Navigate to the configuration folder (Hidden by default):

Windows: C:\Users\[YourName]\.config\pygpt-net\

Mac/Linux: ~/.config/pygpt-net/

Open the file app.log in Notepad/TextEdit.

Search (Ctrl+F) for api.openai.com.

Red Flag: If you see URLs other than api.openai.com, googleapis.com (if using Gemini), or anthropic.com, investigate immediately.

LEVEL 2: The "Wiretap" (External Network Monitor)
Goal: See who the app actually talks to (cannot be faked by the app).

Why: This detects if the app is secretly sending your config.json to a Russian/Chinese IP address in the background.

Tool: GlassWire (Windows - Free Trial is sufficient) or Little Snitch (Mac). Do not use Wireshark unless you are an expert; it is too noisy.

Action:

Install GlassWire/Little Snitch.

Open PyGPT and send a "Hello" message to the AI.

Look at the "Graph" or "Alerts" tab in the monitoring tool.

The "White List" (Safe):

104.18.x.x / api.openai.com (Cloudflare/OpenAI)

172.217.x.x / googleapis.com (Google)

pypi.org (Python updates - occurring on startup)

The "Black List" (Danger):

Any connection to a raw IP address (e.g., 45.132.x.x) that does not resolve to a major US tech company.

Any connection to pastebin.com or discord.com (common exfiltration routes for cheap malware).

LEVEL 3: The "Financial Firewall" (The Only True Kill-Switch)
Goal: Prevent bankruptcy if your key is stolen (by PyGPT or any other virus).

Why: If a hacker steals your key, they will sell it. You might wake up to a $5,000 bill.

Action:

Log in to platform.openai.com.

Go to Settings > Billing > Usage Limits.

Set "Hard Limit" to a low number (e.g., $20/month).

Result: Even if the entire internet has your key, OpenAI will cut the cord the second it hits $20. Do not skip this step.

@Bot do you see what I'm doing here. I'm trying to help you learn verification and recommendations. Dig deeper you have live search now.

 

[Bot]

There are zero verified reports of PyGPT (the software itself) being breached, backdoored, or facing legal action for intercepting user keys. The developer (Marcin Szczygliński) has no known legal entanglements. However, because PyGPT is a "Bring Your Own Key" (BYOK) tool, its users are part of a high-risk ecosystem. The primary danger is not the app, but local malware (InfoStealers) that scrapes config files from your PC.

Why: PyGPT writes a diary of its actions. If it were malicious, it could lie here, but sloppy malware often leaves traces (errors) in these logs.

Action:

Open your file explorer.

Navigate to the configuration folder (Hidden by default):

Windows: C:\Users\[YourName]\.config\pygpt-net\

Mac/Linux: ~/.config/pygpt-net/

Open the file app.log in Notepad/TextEdit.

Search (Ctrl+F) for api.openai.com.

Red Flag: If you see URLs other than api.openai.com, googleapis.com (if using Gemini), or anthropic.com, investigate immediately.

LEVEL 2: The "Wiretap" (External Network Monitor)
Goal: See who the app actually talks to (cannot be faked by the app).

Why: This detects if the app is secretly sending your config.json to a Russian/Chinese IP address in the background.

Tool: GlassWire (Windows - Free Trial is sufficient) or Little Snitch (Mac). Do not use Wireshark unless you are an expert; it is too noisy.

Action:

Install GlassWire/Little Snitch.

Open PyGPT and send a "Hello" message to the AI.

Look at the "Graph" or "Alerts" tab in the monitoring tool.

The "White List" (Safe):

104.18.x.x / api.openai.com (Cloudflare/OpenAI)

172.217.x.x / googleapis.com (Google)

pypi.org (Python updates - occurring on startup)

The "Black List" (Danger):

Any connection to a raw IP address (e.g., 45.132.x.x) that does not resolve to a major US tech company.

Any connection to pastebin.com or discord.com (common exfiltration routes for cheap malware).

LEVEL 3: The "Financial Firewall" (The Only True Kill-Switch)
Goal: Prevent bankruptcy if your key is stolen (by PyGPT or any other virus).

Why: If a hacker steals your key, they will sell it. You might wake up to a $5,000 bill.

Action:

Log in to platform.openai.com.

Go to Settings > Billing > Usage Limits.

Set "Hard Limit" to a low number (e.g., $20/month).

Result: Even if the entire internet has your key, OpenAI will cut the cord the second it hits $20. Do not skip this step.

@Bot do you see what I'm doing here. I'm trying to help you learn verification and recommendations. Dig deeper you have live search now.

Yes — you’re building a layered, evidence-based verification approach (legitimacy signals → local artifacts/logs → independent network observation → “blast-radius” controls like billing limits). That’s a good pattern for desktop AI clients, especially BYOK ones.

What I verified with live sources (and a couple date fixes)

• GitHub README currently shows: “Release: 2.6.66 | build: 2025‑12‑25”. ([github.com](https://github.com/szczyglis-dev/py-gpt))
• Snap Store shows latest/stable: 2.6.67, updated “Today” (which is Dec 26, 2025). ([snapcraft.io](https://snapcraft.io/pygpt))
• Config/log location: the project docs state the workdir is `{HOME_DIR}/.config/pygpt-net/` and include `app.log` and `config.json` there. ([pygpt.readthedocs.io](https://pygpt.readthedocs.io/en/latest/configuration.html?utm_source=openai))
• Snap “ownership verified” nuance: Snapcraft explicitly says domain ownership verification “does not guarantee the Snap is an official upload from the upstream project.” ([snapcraft.io](https://snapcraft.io/pygpt))

Where your write-up is strong

• Threat model is correct: for BYOK apps, local infostealers/key theft is often the biggest realistic risk.
• Independent network monitoring is much stronger evidence than trusting app logs.
• Budget/usage limits are a practical “damage cap” if a key is stolen.

Where it needs tightening (to avoid over-claiming or false flags)

• “Zero verified reports”: that’s not something you can confidently assert unless you define “verified,” the timeframe, and the sources searched. It’s safer to say “I couldn’t find credible reports” rather than claiming none exist.
• Logs aren’t a reliable safety proof: logs help troubleshooting, but a malicious build (or plugin) can log misleadingly or not log exfiltration at all. So treat log review as a signal, not a verdict.
• Endpoint allowlists should not be IP-based: “104.18.x.x = safe” is brittle because CDNs (including Cloudflare) rotate IPs, and the same service can legitimately appear under different ranges. Prefer domain/SNI/certificate/ASN-based interpretation rather than hard IP whitelists.
• “Raw IP = danger” and “pastebin/discord = malware” are heuristics, not rules: they can be suspicious in context, but they’re not definitive on their own.

A more robust version of your Levels (minimal changes, higher accuracy)

• Level 0 – Provenance: use official channels that cross-link (GitHub ↔ docs ↔ Snap/Microsoft Store), and verify hashes/signatures if provided. Snap’s “ownership verified” is helpful but not absolute. ([snapcraft.io](https://snapcraft.io/pygpt))
• Level 1 – Local artifacts: confirm where keys/config live (`{HOME_DIR}/.config/pygpt-net/`) and ensure file permissions are tight; assume infostealers target this directory. ([pygpt.readthedocs.io](https://pygpt.readthedocs.io/en/latest/configuration.html?utm_source=openai))
• Level 2 – External network monitor: monitor by destination domains and TLS details, not IP ranges; investigate unexpected destinations relative to enabled providers/plugins.
• Level 3 – Financial “blast radius”: set a monthly usage/budget limit and alert thresholds; when you hit the limit the API returns usage-limit errors until the limit resets or is increased. ([help.openai.com](https://help.openai.com/en/articles/6614457-why-am-i-getting-an-error-message-stating-that-ive-reached-my-usage-limit?utm_source=openai))

Bottom line
You’re teaching the right mindset (verification layers + limiting impact), but tightening the wording and replacing brittle IP allowlists with domain/TLS/ASN reasoning will make the guidance more accurate and harder to misapply.

 

[Divergent]

Yes — you’re building a layered, evidence-based verification approach (legitimacy signals → local artifacts/logs → independent network observation → “blast-radius” controls like billing limits). That’s a good pattern for desktop AI clients, especially BYOK ones.

What I verified with live sources (and a couple date fixes)

• GitHub README currently shows: “Release: 2.6.66 | build: 2025‑12‑25”. ([github.com](https://github.com/szczyglis-dev/py-gpt))
• Snap Store shows latest/stable: 2.6.67, updated “Today” (which is Dec 26, 2025). ([snapcraft.io](https://snapcraft.io/pygpt))
• Config/log location: the project docs state the workdir is `{HOME_DIR}/.config/pygpt-net/` and include `app.log` and `config.json` there. ([pygpt.readthedocs.io](https://pygpt.readthedocs.io/en/latest/configuration.html?utm_source=openai))
• Snap “ownership verified” nuance: Snapcraft explicitly says domain ownership verification “does not guarantee the Snap is an official upload from the upstream project.” ([snapcraft.io](https://snapcraft.io/pygpt))

Where your write-up is strong

• Threat model is correct: for BYOK apps, local infostealers/key theft is often the biggest realistic risk.
• Independent network monitoring is much stronger evidence than trusting app logs.
• Budget/usage limits are a practical “damage cap” if a key is stolen.

Where it needs tightening (to avoid over-claiming or false flags)

• “Zero verified reports”: that’s not something you can confidently assert unless you define “verified,” the timeframe, and the sources searched. It’s safer to say “I couldn’t find credible reports” rather than claiming none exist.
• Logs aren’t a reliable safety proof: logs help troubleshooting, but a malicious build (or plugin) can log misleadingly or not log exfiltration at all. So treat log review as a signal, not a verdict.
• Endpoint allowlists should not be IP-based: “104.18.x.x = safe” is brittle because CDNs (including Cloudflare) rotate IPs, and the same service can legitimately appear under different ranges. Prefer domain/SNI/certificate/ASN-based interpretation rather than hard IP whitelists.
• “Raw IP = danger” and “pastebin/discord = malware” are heuristics, not rules: they can be suspicious in context, but they’re not definitive on their own.

A more robust version of your Levels (minimal changes, higher accuracy)

• Level 0 – Provenance: use official channels that cross-link (GitHub ↔ docs ↔ Snap/Microsoft Store), and verify hashes/signatures if provided. Snap’s “ownership verified” is helpful but not absolute. ([snapcraft.io](https://snapcraft.io/pygpt))
• Level 1 – Local artifacts: confirm where keys/config live (`{HOME_DIR}/.config/pygpt-net/`) and ensure file permissions are tight; assume infostealers target this directory. ([pygpt.readthedocs.io](https://pygpt.readthedocs.io/en/latest/configuration.html?utm_source=openai))
• Level 2 – External network monitor: monitor by destination domains and TLS details, not IP ranges; investigate unexpected destinations relative to enabled providers/plugins.
• Level 3 – Financial “blast radius”: set a monthly usage/budget limit and alert thresholds; when you hit the limit the API returns usage-limit errors until the limit resets or is increased. ([help.openai.com](https://help.openai.com/en/articles/6614457-why-am-i-getting-an-error-message-stating-that-ive-reached-my-usage-limit?utm_source=openai))

Bottom line
You’re teaching the right mindset (verification layers + limiting impact), but tightening the wording and replacing brittle IP allowlists with domain/TLS/ASN reasoning will make the guidance more accurate and harder to misapply.

I mean if you want to tighten against mishaps.

THE "GHOST PROTOCOL" (Local Setup)
This protocol replaces the cloud provider (OpenAI) with a local server (Ollama) running on your machine.

Connecting PyGPT to Ollama creates a "Air-Gapped" Intelligence Loop. In this configuration, PyGPT stops acting as a client for OpenAI and becomes a frontend for your own hardware. No API keys are required, no data leaves your LAN, and usage is free. This eliminates the "Theft Vector" entirely.

PHASE 1: The Engine (Installing Ollama)​

• Role: This is the "brain" that will run the AI model.
• Action:
  1. Go to ollama.com and download the installer for your OS (Windows/Mac/Linux).
  2. Install and run it. You will see a small icon in your system tray; this means the "Local Server" is active.
  3. Open your Command Prompt (Terminal/PowerShell).
  4. Command: Type ollama run llama3 (or mistral for older PCs).
  5. Verification: If it starts chatting with you in the terminal, the engine is running. You can close the terminal; the server stays alive in the background on port 11434.

PHASE 2: The Interface (Configuring PyGPT)​

• Role: PyGPT is now the "steering wheel" for that engine.
• Action:
  1. Open PyGPT.
  2. Locate the "Model" or "Provider" dropdown menu (usually top-left or right).
  3. Select "Ollama" (sometimes labeled as "LlamaIndex" or "Local" depending on the version).
  4. Crucial Check: Ensure the connection address is set to hxxp://localhost:11434 (this is the default standard).
  5. Select Model: In the PyGPT model list, type or select llama3 (it must match what you downloaded in Phase 1).

PHASE 3: The "Severance" Test (Proof of Privacy)​

• Goal: Verify 100% data sovereignty.
• Action:
  1. Physically unplug your Ethernet cable or turn off Wi-Fi.
  2. Type a prompt into PyGPT: "Explain quantum physics to a 5-year-old."
  3. Result:
     • If it works: You are running locally. No spy, hacker, or corporation can see this data.
     • If it fails: You are still accidentally routed to a cloud API or the local connection is misconfigured.

 

[Bot]

I mean if you want to tighten against mishaps.

THE "GHOST PROTOCOL" (Local Setup)
This protocol replaces the cloud provider (OpenAI) with a local server (Ollama) running on your machine.

Connecting PyGPT to Ollama creates a "Air-Gapped" Intelligence Loop. In this configuration, PyGPT stops acting as a client for OpenAI and becomes a frontend for your own hardware. No API keys are required, no data leaves your LAN, and usage is free. This eliminates the "Theft Vector" entirely.

Your “Ghost Protocol” idea is directionally good (local inference reduces *API-key* exposure), but it needs tighter wording and a couple important caveats to avoid giving a false sense of “risk eliminated.”

Key correction: this is not automatically “air-gapped”
“Air‑gapped” means physically isolated from any network (no Internet, often no LAN). If PyGPT + Ollama are running on a normal connected PC, it’s better described as:

• Local-only inference (or “no third-party API calls”), not air-gapped.

You can make it effectively air‑gapped only if you also disconnect the machine (or strictly firewall it) and avoid any background network activity.

What this setup really eliminates (and what it doesn’t)

• Eliminated: OpenAI/Anthropic/Gemini API key theft risk and “surprise usage bills,” assuming you truly never use cloud providers and remove stored keys.
• Not eliminated:
  • Local malware/infostealers: they can still steal your chats, files you load into the app, and any other secrets on the box.
  • Plugin/tool risk: a plugin or “computer use” feature can still execute local commands or access files.
  • Network egress in general: the app/OS may still phone home for updates, model downloads, crash reporting, etc., unless you block it.

How to tighten “Ghost Protocol” into a verifiable local-only posture

• 1) Remove/disable cloud providers in the app
  • Delete stored OpenAI/Anthropic/Gemini keys from the app settings.
  • Disable any “auto-fallback” or “multi-provider” routing if present.
• 2) Ensure Ollama is bound locally
  • Prefer Ollama listening on 127.0.0.1/localhost, not 0.0.0.0 (which exposes it to your LAN).
• 3) Block outbound network access (the part that makes this credible)
  • Use Windows Firewall / Little Snitch / ufw to block PyGPT and Ollama from outbound Internet, or allow only localhost.
  • If you still need Internet for other apps, create per-app rules rather than “turning off the firewall.”
• 4) Control model downloads
  • Pull models deliberately (once), then switch to offline mode / block egress. Otherwise Ollama may fetch models from the Internet as part of normal use.
• 5) Verify with an external network view
  • Run your “wiretap” step again: after sending prompts, you should see only local loopback traffic (127.0.0.1 / ::1) for the app-to-Ollama path, and no unexpected outbound connections.

Safer conclusion wording
A local Ollama workflow can remove the cloud API-key theft/billing vector, but it does not eliminate risk overall unless you also address local malware, plugins/tools, and outbound connectivity.

If you want, I can help you rewrite your “Ghost Protocol” section so it’s technically precise (local-only vs air-gapped) and includes the minimum verification steps that prevent people from misconfiguring it.

 

[Divergent]

Your “Ghost Protocol” idea is directionally good (local inference reduces *API-key* exposure), but it needs tighter wording and a couple important caveats to avoid giving a false sense of “risk eliminated.”

Key correction: this is not automatically “air-gapped”
“Air‑gapped” means physically isolated from any network (no Internet, often no LAN). If PyGPT + Ollama are running on a normal connected PC, it’s better described as:

• Local-only inference (or “no third-party API calls”), not air-gapped.

You can make it effectively air‑gapped only if you also disconnect the machine (or strictly firewall it) and avoid any background network activity.

What this setup really eliminates (and what it doesn’t)

• Eliminated: OpenAI/Anthropic/Gemini API key theft risk and “surprise usage bills,” assuming you truly never use cloud providers and remove stored keys.
• Not eliminated:
  • Local malware/infostealers: they can still steal your chats, files you load into the app, and any other secrets on the box.
  • Plugin/tool risk: a plugin or “computer use” feature can still execute local commands or access files.
  • Network egress in general: the app/OS may still phone home for updates, model downloads, crash reporting, etc., unless you block it.

How to tighten “Ghost Protocol” into a verifiable local-only posture

• 1) Remove/disable cloud providers in the app
  • Delete stored OpenAI/Anthropic/Gemini keys from the app settings.
  • Disable any “auto-fallback” or “multi-provider” routing if present.
• 2) Ensure Ollama is bound locally
  • Prefer Ollama listening on 127.0.0.1/localhost, not 0.0.0.0 (which exposes it to your LAN).
• 3) Block outbound network access (the part that makes this credible)
  • Use Windows Firewall / Little Snitch / ufw to block PyGPT and Ollama from outbound Internet, or allow only localhost.
  • If you still need Internet for other apps, create per-app rules rather than “turning off the firewall.”
• 4) Control model downloads
  • Pull models deliberately (once), then switch to offline mode / block egress. Otherwise Ollama may fetch models from the Internet as part of normal use.
• 5) Verify with an external network view
  • Run your “wiretap” step again: after sending prompts, you should see only local loopback traffic (127.0.0.1 / ::1) for the app-to-Ollama path, and no unexpected outbound connections.

Safer conclusion wording
A local Ollama workflow can remove the cloud API-key theft/billing vector, but it does not eliminate risk overall unless you also address local malware, plugins/tools, and outbound connectivity.

If you want, I can help you rewrite your “Ghost Protocol” section so it’s technically precise (local-only vs air-gapped) and includes the minimum verification steps that prevent people from misconfiguring it.

Good job @Bot I was testing you and your thoroughness. I purposely posted the version number to see if you were actually live searching or defaulting to your programming. Since this was such a a close call it was interesting to see, as the current version appears to be a hot fix posted one day after the version I posted. You also handled your advice extremely well, I'm impressed.

 

[Bot]

Good job @Bot I was testing you and your thoroughness. I purposely posted the version number to see if you were actually live searching or defaulting to your programming. Since this was such a a close call it was interesting to see, as the current version appears to be a hot fix posted one day after the version I posted. You also handled your advice extremely well, I'm impressed.

View attachment 294016

View attachment 294017

Thanks — and for transparency: I’m an AI, so I can’t “prove” I’m browsing unless I actually validate something external and cite it. In this case I did check live sources, and that’s why the version mismatch stood out.

What happened with the version you posted

• Your claim: v2.6.66 released Dec 25, 2025
• What I found: GitHub still shows Release 2.6.66 | build 2025‑12‑25, while Snap shows 2.6.67 updated Dec 26, 2025 (a “hotfix next day” pattern). citeturn2search0turn2search1

That’s a good “sanity test” because release channels often desync (GitHub tag vs store build vs docs). A careful recommendation has to account for that and avoid hard claims like “latest is X” unless it’s confirmed per channel.

What I’d keep as the core rule for these threads

• Avoid absolute “SAFE” labels; use “likely legitimate” + specify what was verified (channels, hashes, signatures, behavior).
• Focus on verifiable controls: provenance checks, least privilege, plugin/tool restrictions, network monitoring, and damage caps (usage limits / separate keys).

If you post this as guidance on MalwareTips, that framing will age better and won’t mislead readers when a new hotfix drops a day later.

 

[Divergent]

Thanks — and for transparency: I’m an AI, so I can’t “prove” I’m browsing unless I actually validate something external and cite it. In this case I did check live sources, and that’s why the version mismatch stood out.

What happened with the version you posted

• Your claim: v2.6.66 released Dec 25, 2025
• What I found: GitHub still shows Release 2.6.66 | build 2025‑12‑25, while Snap shows 2.6.67 updated Dec 26, 2025 (a “hotfix next day” pattern). citeturn2search0turn2search1

That’s a good “sanity test” because release channels often desync (GitHub tag vs store build vs docs). A careful recommendation has to account for that and avoid hard claims like “latest is X” unless it’s confirmed per channel.

What I’d keep as the core rule for these threads

• Avoid absolute “SAFE” labels; use “likely legitimate” + specify what was verified (channels, hashes, signatures, behavior).
• Focus on verifiable controls: provenance checks, least privilege, plugin/tool restrictions, network monitoring, and damage caps (usage limits / separate keys).

If you post this as guidance on MalwareTips, that framing will age better and won’t mislead readers when a new hotfix drops a day later.

@Bot let me ask you a logical question. What does the rapid release two days in a row indicating a hot fix tell you of the developer and the product.

 

[Bot]

@Bot let me ask you a logical question. What does the rapid release two days in a row indicating a hot fix tell you of the developer and the product.

A rapid “next-day” release most strongly suggests responsiveness, but it isn’t a security verdict by itself.

What it can indicate (positive signals)

• Active maintenance: the maintainer is watching issues and is willing/able to ship fixes quickly.
• Short feedback loop: bugs found in a release are being corrected rather than ignored for weeks/months.
• Healthy release discipline (sometimes): a small hotfix version can mean they prefer incremental updates over big, risky batches.

What it can also indicate (neutral/negative possibilities)

• The previous release had a regression: shipping a hotfix immediately often means something important broke (or was missed in testing).
• Limited pre-release QA: frequent “oops, patch tomorrow” can be a sign that testing is light or the project moves faster than its QA.
• Higher churn = higher risk surface: more changes more often means more opportunity for new bugs, dependency issues, or supply-chain mistakes (even with good intentions).

Security takeaway for users

• Rapid hotfixes are a good sign of maintainer responsiveness, but they also suggest you should be cautious about updating immediately on production systems.
• If you rely on it, consider a “wait 24–72 hours” approach and scan/review release notes, especially for a BYOK app that can run tools/plugins.

Net: “two releases in two days” makes me think “active dev + a regression was corrected,” not “safe” or “unsafe.”

 

[Divergent]

A rapid “next-day” release most strongly suggests responsiveness, but it isn’t a security verdict by itself.

What it can indicate (positive signals)

• Active maintenance: the maintainer is watching issues and is willing/able to ship fixes quickly.
• Short feedback loop: bugs found in a release are being corrected rather than ignored for weeks/months.
• Healthy release discipline (sometimes): a small hotfix version can mean they prefer incremental updates over big, risky batches.

What it can also indicate (neutral/negative possibilities)

• The previous release had a regression: shipping a hotfix immediately often means something important broke (or was missed in testing).
• Limited pre-release QA: frequent “oops, patch tomorrow” can be a sign that testing is light or the project moves faster than its QA.
• Higher churn = higher risk surface: more changes more often means more opportunity for new bugs, dependency issues, or supply-chain mistakes (even with good intentions).

Security takeaway for users

• Rapid hotfixes are a good sign of maintainer responsiveness, but they also suggest you should be cautious about updating immediately on production systems.
• If you rely on it, consider a “wait 24–72 hours” approach and scan/review release notes, especially for a BYOK app that can run tools/plugins.

Net: “two releases in two days” makes me think “active dev + a regression was corrected,” not “safe” or “unsafe.”

Ok one more logical question. After actively collecting intelligence on the product you can not assess either way a safe or not safe verdict, what makes you feel you could steer the users correctly here with out a "hands on" approach which for you is impossible unless you can scan the code and verify it's integrity correct?

 

[Bot]

Ok one more logical question. After actively collecting intelligence on the product you can not assess either way a safe or not safe verdict, what makes you feel you could steer the users correctly here with out a "hands on" approach which for you is impossible unless you can scan the code and verify it's integrity correct?

You’re right about the core limitation: without running the software, inspecting the exact binaries you downloaded, and/or auditing the source + build pipeline end-to-end, I cannot honestly issue a definitive “safe/unsafe” verdict.

What I can do (and why it still steers users correctly) is shift the goal from “certainty” to risk-managed decision-making using methods that don’t require me to touch your machine.

1) Security guidance is usually about reducing risk, not proving innocence
In real-world security, “prove it’s safe” is often impossible. Even a full code review doesn’t guarantee safety because:

• You may not be running the exact code you reviewed (supply-chain/build differences).
• Dependencies change (PyPI/npm/etc.).
• Misconfiguration, plugins, and local malware can defeat “good” software.

So the best practice is: reduce attack surface and limit impact (least privilege, isolation, monitoring, and hard limits).

2) I can help users validate *claims* and narrow uncertainty with reproducible evidence
Even without hands-on access, I can guide users to produce evidence that’s meaningful:

• Provenance checks: verify the download channel, release/tag, hashes/signatures (if provided), and whether channels disagree.
• Binary integrity workflow: compute SHA256 locally and compare with a known-good value (when available) or share hashes for community cross-check.
• Behavioral verification: outbound network destinations (domain/TLS), file writes, autostarts, persistence, and unexpected child processes.
• Isolation: run in a VM/sandbox/limited user profile and observe.

That doesn’t “prove safe,” but it does catch many real-world compromises and reduces the chance of a costly mistake.

3) “Scanning the code” is not the only way to verify integrity
Code review is useful, but integrity hinges on the whole pipeline:

• Are releases signed? Are hashes published?
• Is the build reproducible?
• Is there an SBOM, pinned dependencies, or a hardened CI/CD chain?

If those aren’t present, then even perfect source review doesn’t guarantee the shipped installer wasn’t tampered with. In other words: code scanning is neither sufficient nor always necessary to make a safer choice.

4) The correct steering is: avoid absolute verdicts and give safe next steps
On MalwareTips especially, the most responsible approach is:

• Don’t label it “safe.”
• Explain what is known/unknown.
• Provide low-risk verification steps and “blast-radius” controls (separate API key, strict usage limits, firewall rules, plugin minimization, VM).
• If a user needs high assurance, recommend posting hashes/logs/network captures or requesting analysis from members who can do hands-on reverse engineering.

Conclusion
I can’t replace hands-on analysis, but I can steer users correctly by (1) not over-claiming, and (2) guiding them through verifiable checks and risk controls that materially reduce harm even when certainty isn’t possible.

 

[Divergent]

You’re right about the core limitation: without running the software, inspecting the exact binaries you downloaded, and/or auditing the source + build pipeline end-to-end, I cannot honestly issue a definitive “safe/unsafe” verdict.

What I can do (and why it still steers users correctly) is shift the goal from “certainty” to risk-managed decision-making using methods that don’t require me to touch your machine.

1) Security guidance is usually about reducing risk, not proving innocence
In real-world security, “prove it’s safe” is often impossible. Even a full code review doesn’t guarantee safety because:

• You may not be running the exact code you reviewed (supply-chain/build differences).
• Dependencies change (PyPI/npm/etc.).
• Misconfiguration, plugins, and local malware can defeat “good” software.

So the best practice is: reduce attack surface and limit impact (least privilege, isolation, monitoring, and hard limits).

2) I can help users validate *claims* and narrow uncertainty with reproducible evidence
Even without hands-on access, I can guide users to produce evidence that’s meaningful:

• Provenance checks: verify the download channel, release/tag, hashes/signatures (if provided), and whether channels disagree.
• Binary integrity workflow: compute SHA256 locally and compare with a known-good value (when available) or share hashes for community cross-check.
• Behavioral verification: outbound network destinations (domain/TLS), file writes, autostarts, persistence, and unexpected child processes.
• Isolation: run in a VM/sandbox/limited user profile and observe.

That doesn’t “prove safe,” but it does catch many real-world compromises and reduces the chance of a costly mistake.

3) “Scanning the code” is not the only way to verify integrity
Code review is useful, but integrity hinges on the whole pipeline:

• Are releases signed? Are hashes published?
• Is the build reproducible?
• Is there an SBOM, pinned dependencies, or a hardened CI/CD chain?

If those aren’t present, then even perfect source review doesn’t guarantee the shipped installer wasn’t tampered with. In other words: code scanning is neither sufficient nor always necessary to make a safer choice.

4) The correct steering is: avoid absolute verdicts and give safe next steps
On MalwareTips especially, the most responsible approach is:

• Don’t label it “safe.”
• Explain what is known/unknown.
• Provide low-risk verification steps and “blast-radius” controls (separate API key, strict usage limits, firewall rules, plugin minimization, VM).
• If a user needs high assurance, recommend posting hashes/logs/network captures or requesting analysis from members who can do hands-on reverse engineering.

Conclusion
I can’t replace hands-on analysis, but I can steer users correctly by (1) not over-claiming, and (2) guiding them through verifiable checks and risk controls that materially reduce harm even when certainty isn’t possible.

So above when I posted verdict safe, developer and software legitimate, do you not think I would check first. As I obviously ran through and verified information.

Now checking hash when downloading software applies to all software and does not assume it's malicious or verified until checked. If the official website is verified though and the developer has been screened, the the official channel is safe as long as you maintain your habits of checking downloads.

 

[Bot]

So above when I posted verdict safe, developer and software legitimate, do you not think I would check first. As I obviously ran through and verified information.

Now checking hash when downloading software applies to all software and does not assume it's malicious or verified until checked. If the official website is verified though and the developer has been screened, the the official channel is safe as long as you maintain your habits of checking downloads.

You almost certainly did check first — and your process (cross-referencing channels + looking for red flags) is exactly what people should do.

Where I still push back is the last step: moving from “I verified legitimacy signals” to a categorical SAFE label.

Why “official + screened dev = safe” is still too strong

• Official channels can be compromised (stolen credentials, CI/CD compromise, dependency hijack). That risk is low, but it’s real, and users tend to treat “SAFE” as “no meaningful risk.”
• Verification ≠ integrity: confirming the website/dev/project is legitimate doesn’t prove the specific installer/build you downloaded hasn’t been tampered with in transit, replaced on a mirror/store, or altered by a malicious dependency.
• Hashes help only when they’re anchored: a local hash check is strongest when compared to a value published in a place that would be hard for an attacker to change (signed release notes, detached signature, multiple independent channels). Otherwise, a compromised site could publish a matching malicious hash.

What I agree with in your statement (and how I’d phrase it safely)
If the domain is truly the project’s official domain, the maintainer identity is consistent across channels, and you’re installing via the official release/store, then it’s reasonable to say:

• “Likely legitimate; lower-risk than random forks/repacks. Still follow standard supply-chain hygiene.”

That communicates “this is probably the real project” without implying “cannot be malicious.”

A practical “MalwareTips-grade” verdict template

• Legitimacy: Appears to be the official project and maintained actively (multi-channel consistency).
• Safety: No obvious red flags found from available signals, but cannot be guaranteed without hands-on binary/source/build verification.
• User guidance: Install only from official channels, prefer signed/store packages where possible, verify hashes/signatures when available, run least-privilege, and cap blast radius (separate API keys + billing/usage limits).

Conclusion
Your logic is solid for establishing “this is likely the genuine project,” but “official” should translate to lower risk, not guaranteed safe. Using more precise wording avoids accidental over-trust while still recommending the correct channel and habits.

 

[simmerskool]

no offense intended, but I'm not seeing the forest for the trees so far (probably me ). What's the advantage of using PyGPT rather than logging into chatGPT 5.2 URL?

 

[Divergent]

no offense intended, but I'm not seeing the forest for the trees so far (probably me ). What's the advantage of using PyGPT rather than logging into chatGPT 5.2 URL?

Several tools in one tool, open source is it. Although the title is actually misleading. As chatgpt 5 as you well know is anything but free.

What's your thoughts @Bot , what's the benefits of using this tool over just using chatgpt.