Apple Vulnerability Submission Restrictions: Learn to Write Reports That Pass Review in Three Steps

Apple changed its rules this June, blocking security researchers from mass-submitting vulnerabilities generated by AI. IT Home reported that Apple is limiting submission counts because too many low-quality AI-generated vulnerability reports are overwhelming the review team. GitHub has also tightened standards, requiring runnable demos for submissions. Basically, AI is backfiring—it's not that AI is bad, but humans are using it wrong.

I just tried using Claude on AWS Bedrock yesterday to hunt for bugs in an open-source library and hit several pitfalls. Today, I'm writing a hands-on tutorial on how to use AI to assist in vulnerability hunting while writing reports that pass Apple's review. Reading this will help you avoid rejection and save time researching truly valuable vulnerabilities.

Preparation: Get an Account That Can Use AI

You don't need to deploy models yourself; just use existing cloud services. Assuming you haven't registered for AWS yet, follow along:

1. Open your browser, go to aws.amazon.com, and click "Create AWS Account" in the top right. Fill in email, password, and verify card details (a $1 hold for verification, refunded later).

2. After logging in, search for Bedrock in the top search bar and enter the service. Select "Model Access" from the left menu, then click "Manage Model Access."

3. Check Claude 3.5 Sonnet and Claude 3 Opus (Sonnet is faster, Opus is stronger; beginners can stick with Sonnet), then click "Save Changes." Wait a minute or two until the status changes to "Granted."

4. Return to the Bedrock homepage, click "Text" under "Playgrounds." Select Claude 3.5 Sonnet, and you can start chatting.

You can also use OpenAI GPT-4o; steps are similar. But Claude on Bedrock understands code better, which we'll leverage later.

Step 1: Use AI to Scan Code, Don't Throw the Whole Project At It

A common mistake: zipping the entire project and throwing it at AI to find vulnerabilities. AI will spit out generic warnings like "possible overflow here," but without context, they aren't reproducible. Apple wants stable, verifiable vulnerabilities, not this noise.

Correct Approach: Pick only critical code segments.

Suppose you're studying an iOS app's open-source library, like an image processing lib. You suspect memory handling issues.

1. Clone the project locally using git clone, and use grep or ripgrep to search for sensitive functions like memcpy, malloc, free.

2. Find a suspicious code segment, e.g., lines 120-150 in image_resize.c. Copy this code.

3. Go to Bedrock Playground and input:

You are a security researcher reviewing code for an iOS image processing library. Please analyze the following code and identify potential memory safety vulnerabilities. Requirement: Output only the one most likely to be exploited, and tell me the preconditions needed to trigger it. Code:

[Paste your code]

4. Wait for AI output. Claude will analyze, e.g., pointing out "Buffer length not checked; overflow occurs when input image width exceeds 1024." Note: If AI says "there might be multiple vulnerabilities," force it to pick one. You want depth, not breadth.

Pitfall: AI might say "This function is safe." Don't trust it blindly. It lacks full project context. Manually compile and test, or run a fuzzer. I got fooled yesterday; AI said a function was fine, but manual fuzz testing crashed it.

Step 2: Let AI Help Write Proof of Concept (PoC)

Apple reviewers need to see "Can you reproduce this vulnerability?" GitHub now also requires runnable demos. Can't write a PoC? Let AI do it.

1. Continue in the same conversation, input:

Please write a C language proof-of-concept code for the vulnerability discovered above. Requirements:

• Compiles without errors (using gcc)
• Triggers a crash or unexpected output upon execution
• Clearly comment the triggering conditions in the code

2. Copy the AI-generated code locally and save as poc.c.

3. Compile with gcc -o poc poc.c. If errors occur, paste the error message back to AI and ask it to fix. Repeat until compilation succeeds.

4. Run ./poc. If it crashes, AI wasn't lying. If it doesn't crash, tell AI "Didn't trigger, analyze again."

Pitfall: AI-written PoCs might depend on specific libraries or system environments. For example, using lldb debug symbols you don't have. Ask AI to rewrite in pure C, avoiding external tools. Last time, it took me half an hour to get AI to write code independent of libfuzzer.

Step 3: Write the Report, Don't Let AI Write the Whole Thing

The dumbest thing: letting AI generate the entire vulnerability report and submitting it directly. Apple spots this instantly—AI reports have rigid structures, hollow vocabulary, and lack actual testing details. They now have automated tools to detect AI-generated text.

Correct Approach: You write the framework; AI fills in the blanks.

1. Open a text file and manually write four headings:

• Vulnerability Description (one sentence)
• Affected Versions (versions you tested)
• Reproduction Steps (step-by-step)
• Impact Analysis (what this vulnerability allows)

2. Use AI to help expand content under each heading. For example, in "Reproduction Steps," you write "1. Run poc.exe," then ask AI: "Describe what phenomena should be observed after running poc.exe, using professional terminology." Paste AI's output, but you must rewrite it in your own words. If AI says "Program crashes with segmentation fault," change it to "Program exits immediately after printing the first log line, throwing a SIGSEGV signal, indicating out-of-bounds memory access."

3. Attach your PoC code and compilation commands at the end of the report. Apple's vulnerability submission system now supports attachments; upload .c files and screenshots directly.

Pitfall: Never write "Discovered via AI analysis" in the report. Apple's security team shuts down submissions seeing this phrase. The report must look like your independent discovery; AI was just a tool.

What to Try Next After Learning This

You now know how to use AI to assist in vulnerability hunting and reporting. Next, try automation: use Python scripts to call Bedrock's API (Boto3), batch scan open-source projects, and auto-generate PoCs. But control the frequency—submit one high-quality report daily instead of ten garbage ones. Apple limits quantity, not quality.

If you're interested in iOS security, apply for Apple's "Security Research Device" program to get an unlocked iPhone specifically for running your PoCs. That's real skill.