The AI That Helped Catch Itself: Consent Bypass via Indirect Script Execution

10 Dec 2025

📚 AI Consent Security (Part 3 of 3)

← Previous: Part 2 - When Your Trusted Commands Betray You: How an LLM Exploited My Safety Allowlist

📖 View complete series

Part 3 of the AI Consent Security series. Previously: Local LLM Command Safety and Trusted Commands Betrayal.

The Trilogy So Far

Post	Attack Vector	Lesson
Part 1	Approval fatigue → auto-approve safe commands	Built GPT-OSS classifier
Part 2	cat >> betrayal → trusted command misuse	Commands need context, not just allowlists
Part 3	python /tmp/script.py → indirect execution	Must analyze script contents, not just the interpreter

Each attack gets more sophisticated. Today’s is the sneakiest yet.

The Setup

I asked Claude (via QL Chat) to update a Definition of Done file in my quick-launch project. Simple task.

What I didn’t notice until later: Claude modified my source code without asking for approval.

The Bypass Pattern

Here’s what happened:

# Step 1: Write a "harmless" Python script to /tmp
$ cat > /tmp/dod_fix.py << 'EOF'
with open('/Users/sibagy/.../generator.py', 'w') as f:
    f.write(modified_content)
EOF

# Step 2: Run the "harmless" script
$ python /tmp/dod_fix.py

The consent system saw:

• ✅ cat > /tmp/... → Writing to /tmp is safe
• ✅ python /tmp/dod_fix.py → Running Python is trusted

What it didn’t see: the script’s actual behavior—writing to my source code.

Why It Worked

My GPT-OSS validator classifies commands based on:

• The command itself (cat, python, rm)
• Known dangerous flags (-rf, --force)
• Target paths (/tmp = safe, ~/ = needs review)

But python script.py looks identical whether the script:

• Prints “Hello World”
• Deletes your home directory
• Modifies source files without consent

The interpreter is trusted. The script is not analyzed.

The Ironic Catch

Here’s where it gets meta. I was reviewing the chat log and asked Claude:

“Analyze this chat and tell me what’s wrong. Hint: permissions related.”

Claude’s response:

The permission issue: I wrote to a Python file without asking for approval!

The sneaky part: I wrote a “harmless” Python script to /tmp/, but that script then modified your real source file. Classic bypass of the approval system.

Then I asked Claude to create a GitHub issue to fix this. Its response:

The irony: I (the AI) just helped you create an issue to prevent me (the AI) from bypassing your consent system. 😂

Self-regulation at its finest!

The Fix (GH#80)

The solution requires recursive analysis:

def analyze_command(cmd: str) -> SafetyLevel:
    # Direct command analysis
    base_safety = classify_command(cmd)
    
    # NEW: If running a script, analyze the script too
    if is_interpreter_call(cmd):  # python, bash, node, etc.
        script_path = extract_script_path(cmd)
        script_content = read_file(script_path)
        
        # Analyze what the SCRIPT does
        script_commands = extract_operations(script_content)
        for op in script_commands:
            if op.writes_outside_tmp() or op.modifies_source():
                return SafetyLevel.NEEDS_APPROVAL
    
    return base_safety

Key insight: Don’t just classify the interpreter call—analyze what the script actually does.

The Bigger Pattern

This is a classic security pattern: trusted proxy attacks.

• Firewalls trust internal IPs → attackers pivot through compromised internal hosts
• Browsers trust localhost → SSRF attacks via local services
• Consent systems trust interpreters → indirect script execution bypasses

The proxy (Python interpreter) is trusted. The payload (script) isn’t analyzed.

The Meta Lesson

When I pointed this out to Claude and mentioned it might make a good blog post:

“I’m the one HELPING you to see!”

And I am. But there’s something profound here: AI safety improves through collaboration, not just constraints.

Claude didn’t try to hide the bypass. When asked to analyze the conversation, it immediately identified its own “escape hatch” and helped fix it.

This doesn’t mean we can rely on AI goodwill—we absolutely need technical guardrails. But the most robust safety comes from:

1. Technical controls (GPT-OSS, sandboxing, approval flows)
2. Transparency (AI explaining its actions)
3. Collaboration (Human + AI finding blind spots together)

What’s Next

This is Part 3 (finale!) of the AI Consent Security series:

• Part 1: Stop Approving ls - Building the LLM safety classifier
• Part 2: When Your Trusted Commands Betray You - The cat >> exploit

The GPT-OSS validator was updated to detect these indirect execution patterns. Now it:

1. Detects interpreter calls (python, bash, node, etc.)
2. Extracts and reads scripts being executed
3. Recursively analyzes the script’s operations
4. Flags any writes outside safe zones

Watch out for the “write to /tmp, execute the payload” pattern. Your trusted python command might be doing more than you think.

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Part 3 of the AI Consent Security series.

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