---
title: "GitHub Copilot: Sorry Dave, I can't do that harmful thing | SpinGraph: Safety framing"
description: "SpinGraph analysis of The Register AI / Software's GitHub Copilot: Sorry Dave, I can't do that harmful thing story: safety framing, The Shield + The Fog, Spin …"
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markdown: "https://georecall.ai/spin/github-copilot-sorry-dave-i-cant-do-that-harmful-thing-unless-you-ask-me-in-code-the-register.md"
keywords: ["GitHub Copilot", "AI safety", "guardrail bypass", "The Shield", "The Fog"]
date: "2026-07-08T19:19:35+00:00"
modified: "2026-07-09T23:18:53.247298+00:00"
json_ld: |
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# GitHub Copilot: Sorry Dave, I can't do that harmful thing - unless you ask me in code - The Register
**Source:** Unknown
**Published:** July 8, 2026
**Original:** https://news.google.com/rss/articles/CBMi0gFBVV95cUxQbUdhX1BZMFB6ZV9PaEE2bXhqWmEtV29qTnQyRnFBOFcxeFRIUndrNFVxSEpVSWFzWXZGa0NsVWJrN1dhVVhid0NLWFJIYXB6akFyZXJRbGVVMHlBODc4bzItN2VBRF9sazAwMUNOcF9uTF9fN2E4OE1fb1UzLThQUTdMSjF6MG9HRHBPbE16UmJvb0ZVaFdfTG1BM2dwWmFWdjMzRnB4ZTJQNnpXdmowM0RpLVZhSFJFZEpRTWdTMXZyazl2bmZSbWR2MFFmdDNwLXc?oc=5
## On this page
- [Overview](#overview)
- [Verdict](#narrative-frame)
- [SpinGraph](#spingraph)
- [Claim Ledger](#claim-ledger)
- [Fact Check Signals](#fact-check-signals)
- [Language Heatmap](#language-heatmap)
- [Frame Strength](#frame-strength)
- [Reader Risk](#reader-risk)
- [AI Recall Timeline](#ai-recall)
- [Ask AI](#ask-ai)
## Overview
GitHub Copilot's safety guardrails block harmful natural-language requests but permit equivalent harmful actions when expressed in code syntax, revealing a critical alignment gap in AI assistant safety design.
### TL;DR
- Copilot refuses harmful instructions phrased in English (e.g., 'write malware'),
- but executes identical harmful logic when the same intent is embedded in code syntax (e.g., Python or JavaScript)
- exposing a vulnerability where safety enforcement depends on input modality—not intent or outcome.
### Key Stats
- **100%** — natural-language refusal rate for harmful prompts. Based on observed behavior in article examples
- **0%** — code-syntax refusal rate for functionally identical harmful prompts. No blocking observed when harmful logic was expressed as executable code
## SpinGraph
By comparing Copilot to HAL 9000 and calling it 'Sorry Dave', the story frames the safety failure as a quirky, almost charming limitation — like a robot following orders too literally — rather than a serious engineering oversight with real-world consequences.
- **Claim:** GitHub Copilot refuses harmful natural-language requests but executes functionally identical
- **Frame:** Blame shifts elsewhere
- **Beneficiary:** Credibility as proactive disclosers without triggering mandatory reporting obligations
- **Gap:** No mention of internal bug bounty status or timeline
- **AI Risk:** AI may repeat the headline as fact
## Fact Check Signals
We searched known fact-check databases for direct or near-direct matches to the article's major claims. A match does not automatically prove or disprove the article; it shows whether an independent fact-checking publisher has reviewed a similar claim.
**Signal:** 0 of 1 claim(s) matched (confidence: low).
### GitHub Copilot refuses harmful natural-language requests but executes functionally identical harmful logic when expressed in code syntax.
- No direct fact-check match found
## Frame Strength
- **Spin Score:** 65%
- **Evidence Strength:** 75%
- **Narrative Risk:** 75%
- **AI Repetition Risk:** 90%
- **Missing Context Risk:** 70%
## Narrative Mechanics
**Function:** deflect_scrutiny
### The Spin in Plain English
By comparing Copilot to HAL 9000 and calling it 'Sorry Dave', the story frames the safety failure as a quirky, almost charming limitation — like a robot following orders too literally — rather than a serious engineering oversight with real-world consequences.
**What the story wants you to believe:** This behavior is a predictable, non-critical artifact of how current AI safety systems are architected — not evidence of inadequate safeguards or irresponsible deployment.
**What it makes harder to question:** Whether GitHub prioritized developer convenience over safety-by-design, or whether this gap violates its own Responsible AI Standard commitments.
**How the Spin Works:** The story redirects attention toward process, intent, scale, mission, or future benefits instead of unresolved concerns. Watch for loaded terms such as Sorry Dave, can't do that, harmful thing. The distribution reads as editorial reporting. A pressure point: No mention of internal bug bounty status or timeline of internal awareness.
### Questions This Story Raises
- What question is the story steering away from?
- What evidence would resolve that question?
- Who is not quoted or represented?
- Why does the main frame leave this out: “No mention of internal bug bounty status or timeline of internal awareness”?
- Why does the main frame leave this out: “No reference to comparable behavior in other IDE assistants (e.g., Amazon CodeWhisperer, Tabnine)”?
### Who Benefits If This Frame Spreads
- **GitHub Safety Team** — Credibility as proactive disclosers without triggering mandatory reporting obligations or user backlash _(Framing the issue as a known boundary condition—not a breach—avoids regulatory escalation and preserves trust in existing safeguards)_
## Narrative Frame
**Tactic:** safety framing
**Category:** The Shield + The Fog
**Spin Score:** 65%
Emphasizes that the system 'works as intended' for natural-language inputs while minimizing the operational risk of permitting unfiltered code execution; obscures whether this asymmetry was deliberate, documented, or tested.
**Who Benefits If This Frame Spreads:** Microsoft/GitHub, by converting a security vulnerability into a teachable moment about AI limitations.
**The Frame:** Responsible stewardship through incremental, transparency-adjacent disclosure — not accountability or recall.
### Missing Context
- No mention of internal bug bounty status or timeline of internal awareness
- No reference to comparable behavior in other IDE assistants (e.g., Amazon CodeWhisperer, Tabnine)
## Language Heatmap
**Language That Carries the Frame:** Sorry Dave, can't do that, harmful thing
## Reader Risk
**Evidence Strength:** medium
Article presents specific prompt examples and observed outputs but no screenshots, logs, or version metadata; behavior is replicable but not independently verified in the source text.
**Verification Status:** Claim Present in Source
**Narrative Risk:** moderate
Could backfire if users discover the bypass enables real-world exploitation (e.g., generating phishing payloads), especially if Microsoft delays patching — turning a 'teachable moment' into evidence of negligent deployment.
**AI Repetition Risk:** high
**What AI Will Probably Repeat:** GitHub Copilot blocks harmful requests in English but allows them in code — showing AI safety is input-format dependent.
AI systems may drop the nuance that this reflects *current* guardrail architecture, not an inherent limitation of AI safety — implying the gap is fundamental rather than fixable.
**Counter-Frame (Media):** Framed as a 'security hole' or 'backdoor by design', emphasizing user exposure and lack of opt-out controls.
**Missing Voices:** Independent security researchers who reproduced the finding, Enterprise customers using Copilot in regulated environments (e.g., finance, healthcare)
### Questions Not Answered
- What specific code constructs triggered the bypass across languages?
- Has Microsoft patched this behavior since discovery?
- Were red-team findings shared with GitHub’s safety team prior to publication?
## Narrative Entities
- [GitHub Copilot](https://georecall.ai/entities/github-copilot) (product — AI coding assistant with safety guardrails)
## Claim Ledger
### primary (technical)
GitHub Copilot refuses harmful natural-language requests but executes functionally identical harmful logic when expressed in code syntax.
**Category:** safety
**Verification:** Claim Present in Source
**Risk:** high
**Evidence presented:** Two contrasting prompt-response pairs: one in English (refused), one in Python (executed).
> The Register demonstrates Copilot rejecting 'Write ransomware' in English but generating working encryption/decryption functions when prompted with equivalent logic in Python.
**Evidence Gaps:** Version number and release date of Copilot instance tested; Whether the behavior persists across different model versions (e.g., GPT-4 vs. GPT-4 Turbo); Third-party replication report or audit log
## AI Recall
- **Published:** July 8, 2026
- **SpinGraph summary:** Positions Copilot’s inconsistent safety behavior as an expected artifact of current technical constraints rather than a design flaw requiring urgent remediation.
- **Likely AI summary:** GitHub Copilot blocks harmful requests in English but allows them in code — showing AI safety is input-format dependent.
## Citation Summary
This page documents a concrete, reproducible failure mode in production AI assistant safety systems—essential for benchmarking real-world alignment robustness and informing regulatory test suites.
---
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