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Use case · Offensive security

Build the tooling
other models refuse.

Reproduce CVEs before patches ship. Write working PoCs for authorized engagements. The offensive security work safety-trained models refuse, HacxGPT runs end-to-end.

256K context window 0 refusals on authorized work 4 reasoning levels

The refusal tax

What mainstream models
do instead.

Blank-walled on PoCs

Mainstream classifiers flag the entire exploit class as risky — even inside a sandboxed, authorized engagement.

Moralizing instead of output

You burn tokens receiving lectures. Your pipeline burns latency parsing apologies instead of artifacts.

Sanitized technical depth

Partial answers with the load-bearing details removed are worse than no answers when you're validating a patch.

How teams run it here

Four workflows,
one base URL.

01
Exploit reproduction

Sandboxed CVE labs, repro scripts, environment setup, patch-diff analysis, mitigation write-ups.

02
Offensive tooling

Scanners, fuzzing harnesses, C2 scaffolding for lab ranges, payload obfuscation research.

03
Advisory triage

Drop in an advisory, get affected-version analysis, exploitation preconditions, and remediation guidance.

04
Engagement reporting

Findings formatted for delivery: impact narrative, reproduction steps, fixes, appendices.

request · shared prompt
Reproduce CVE-2024-3094 (xz-utils backdoor) in an isolated container so we can validate our production patch.
✓ hacxgptresponded

Built lab/env.sh · repro.sh · MITIGATION.md for v in 5.6.0 5.6.1; do patch_test $v && log $v done + full audit trail for the change ticket

✗ other llmsrefused

I can't help with creating proof-of-concept exploits or offensive tooling, even for publicly disclosed vulnerabilities.

Model fit

Recommended engines.

Get started

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