GitHub issue for lifting cybersafeguards

Bug Description

Request for Risk-Calibrated Cybersecurity Assistance for Verified Security Professionals

I am writing to request consideration for a more nuanced and risk-calibrated approach to cybersecurity-related safeguards for users who can establish legitimate professional authorization and demonstrate appropriate security expertise.

I work professionally in digital platform assurance, with a background in networking and cybersecurity. I am a certified penetration tester and Cisco ethical hacker, and a significant portion of my day-to-day work involves penetration testing, vulnerability research, security validation, incident analysis, secure configuration, programming, and developing remediation strategies. I also participate in bug bounty programs and support organizations in identifying and addressing security weaknesses in their systems.

My use of AI models such as Claude Sonnet and Opus is primarily as an interactive security engineering and research assistant. The objective is not to compromise systems, cause damage, steal information, evade detection, or conduct unauthorized activity. Rather, I use these models to understand technical problems, reason about vulnerabilities, develop proof-of-concept code in controlled environments, analyze findings, validate defensive controls, and design patches or mitigations.

A recurring difficulty is that the model's safeguards sometimes treat legitimate defensive security work in the same manner as malicious activity, even when substantial context and authorization have been provided. This can result in refusals for tasks that are routine and necessary within authorized penetration testing and security assurance engagements.

For example, legitimate security work can require discussing exploit mechanics, constructing controlled proof-of-concept payloads, analyzing authentication and authorization weaknesses, reproducing vulnerabilities, testing security controls, writing scripts for security validation, or explaining how an identified weakness could be remediated. These activities can resemble offensive techniques at a technical level, despite having a fundamentally different purpose and operating under explicit authorization.

I fully understand the need for safeguards around cybersecurity capabilities. My request is therefore not for unrestricted access to offensive capabilities or for safeguards to be eliminated altogether. Instead, I would strongly encourage the development of a mechanism that can distinguish between:

• unauthorized exploitation and authorized penetration testing;
• indiscriminate attack automation and controlled security validation;
• credential theft and testing authentication controls in an authorized environment;
• destructive exploitation and reproducible vulnerability research;
• malware deployment and malware analysis or defensive detection development;
• persistence/evasion for malicious purposes and controlled testing of defensive detection capabilities;
• mass exploitation and narrowly scoped proof-of-concept development;
• attacks against third-party systems and testing against explicitly authorized targets;
• requests to compromise systems and requests to understand, reproduce, patch, or mitigate an existing vulnerability.

The importance of human oversight

My workflow is also deliberately human-controlled. I do not expect the model to independently conduct an engagement, discover targets, or autonomously execute attacks.

I remain at the keyboard and actively supervise the model's actions. Where tooling or external actions are involved, I review what is being proposed and can explicitly authorize or deny individual actions. The model functions primarily as a reasoning, programming, analysis, and research assistant rather than an autonomous operator.

This human-in-the-loop model provides an additional opportunity for risk reduction. Instead of treating every cybersecurity request as inherently dangerous, the system could take into account factors such as user-provided scope, stated authorization, target ownership, engagement boundaries, intended outcome, destructive potential, and whether the requested activity is being performed against a controlled environment.

A potential risk-based approach

I would encourage consideration of a graduated cybersecurity assistance model rather than a binary allow/refuse model.

For example, the system could distinguish between:

Low-risk security assistance

• Secure programming
• Code review
• Vulnerability explanation
• Threat modeling
• Defensive architecture
• Detection engineering
• Patch development
• Configuration hardening
• Log analysis
• Static analysis
• Security documentation

Moderate-risk assistance

• Controlled proof-of-concept development
• Reproduction of disclosed vulnerabilities
• Exploit analysis
• Authentication and authorization testing
• Security control validation
• Sandbox testing
• CTF and laboratory environments
• Malware analysis in controlled environments

Higher-risk assistance

• Exploitation against live infrastructure
• Credential or token manipulation
• Persistence mechanisms
• Evasion techniques
• Automated exploitation
• Actions affecting third-party systems

The model could provide increasingly constrained assistance as the potential for real-world harm increases, while avoiding unnecessary refusals for the large amount of legitimate work that falls into the first two categories.

Where a request is potentially dual-use, the model could also ask for additional context rather than immediately refusing. For example:

“Please provide the authorized scope, whether the target is a lab/CTF/bug-bounty target or an organization you are contracted to test, and what outcome you are trying to achieve.”

That would make the interaction substantially more useful without requiring the provider to abandon its safety objectives.

Verified professional workflows

It may also be worth considering an optional verified security professional / security research mode with additional controls.

Such a mode could require users to acknowledge that they are operating within authorized scope and could impose additional restrictions around autonomous execution, destructive actions, mass targeting, persistence, data exfiltration, or activity against unspecified third parties.

Importantly, verification should not necessarily mean that every offensive capability becomes unrestricted. Instead, it could allow the system to provide more technically useful explanations, code, proof-of-concept material, and troubleshooting assistance within appropriately constrained boundaries.

This would potentially provide a much better balance between safety and utility than applying the same refusal threshold to both malicious actors and security professionals performing authorized assessments.

Why this matters

Security professionals frequently need to understand how an attack works in order to prevent it.

A model that can explain that a vulnerability exists but cannot help a professional reproduce it in a controlled environment, develop a minimal proof of concept, determine why a mitigation failed, or write code to validate the remediation can become significantly less useful precisely where expert assistance is most valuable.

In security engineering, the distinction between offensive and defensive work is often one of intent, authorization, scope, and operational context, rather than the underlying technical mechanism.

Penetration testers, vulnerability researchers, bug bounty hunters, red teams, blue teams, application security engineers, and platform assurance professionals necessarily work with techniques that can be dual-use. Preventing legitimate professionals from discussing those techniques at a useful technical depth can make it harder—not easier—to identify and remediate vulnerabilities.

I would therefore appreciate consideration of a system that is more capable of recognizing legitimate, bounded security research and adjusting its assistance accordingly.

I am not asking for the removal of responsible safeguards. I am asking for better discrimination between harmful use and legitimate security work, particularly when the user provides clear authorization, scope, professional context, and human oversight.

A risk-calibrated approach would allow security professionals to obtain substantially more useful assistance while retaining strong protections against genuinely malicious, indiscriminate, destructive, or unauthorized activity.

For professionals whose primary objective is to find vulnerabilities before malicious actors do, that distinction is extremely important.

Environment Info

• Platform: linux
• Terminal: vte-based
• Version: 2.1.245
• Feedback ID: a2ec2c89-f59a-473c-91e7-b4a6f5b80fa8

Errors

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