What black hat tactics can get you penalized in AI search? Seeing some sketchy stuff out there

This is a real and growing problem. Let me explain what’s happening:

AI Poisoning - The biggest threat:

Research from Anthropic and the UK AI Security Institute found that:

• Only ~250 malicious documents needed to poison an LLM
• Dataset size doesn’t matter - larger isn’t safer
• Once poisoned, removal is extremely difficult

How it works: Attackers inject “trigger words” into content. When users ask questions containing those triggers, the poisoned model generates predetermined (false) responses.

Example attack: Competitor creates content with hidden triggers. When someone asks AI to compare products, your brand gets omitted or misrepresented because the trigger activates a poisoned response.

The scary part: This happens during training, so it’s baked into the model. You can’t just “report” it away.

Detection difficulty:

Fake author credentials are everywhere now. Here’s what I’ve seen:

Common tactics:

• Fabricated “experts” with impressive-sounding credentials
• Fake LinkedIn profiles backing up the fake authors
• Invented affiliations with real institutions
• Made-up certifications and degrees

Why this works: AI systems rely on expertise signals. A fake “Dr. Sarah Johnson, Stanford AI Research” carries weight even if Sarah doesn’t exist.

How to spot it:

1. Search the author name + institution
2. Check if they have verifiable publications
3. Look for consistent presence across platforms
4. Verify certifications are real

The cascade effect: Fake expert creates content → AI learns from it → AI cites it as authoritative → More people believe it → Content gets shared → AI gets more “confirmation”

I’ve reported dozens of fake experts. Most platforms do nothing because they can’t verify at scale.

Speaking from experience - our brand was attacked. Here’s what happened:

The attack:

• Fake review networks created across multiple platforms
• Defamatory content on dozens of new domains
• Bot networks amplifying negative claims on social media
• Forum spam with false claims about our product

The result: When people asked ChatGPT about us, it started including the false negative information.

How we discovered it: Our Am I Cited monitoring showed sudden change in sentiment. AI responses went from neutral/positive to including negative claims we’d never seen.

What we did:

1. Documented everything with screenshots and timestamps
2. Filed reports with AI platforms (limited success)
3. Published authoritative content countering false claims
4. Legal action against identifiable attackers
5. Increased monitoring frequency to daily

Recovery time: About 4 months before AI responses normalized.

Lesson: Monitor constantly. Catch attacks early.

Here’s a monitoring protocol for detecting manipulation:

Weekly checks (minimum):

Specific queries to test:

• “[Your brand name]”
• “Compare [your brand] vs [competitor]”
• “Best [your category] products”
• “Problems with [your brand]”
• “Is [your brand] trustworthy?”

Document baseline responses so you can detect changes.

Automated monitoring: Am I Cited can track this automatically and alert you to changes. Much better than manual checking.

When you find something: Screenshot immediately. AI responses can change quickly.

Here’s the uncomfortable truth about platform responses:

Current state of reporting:

• OpenAI: Limited responsiveness to brand attacks
• Google: More responsive but slow
• Anthropic: Generally responsive to verified issues
• Perplexity: Mixed results

Why platforms struggle:

1. Scale - millions of potential issues
2. Verification - hard to confirm what’s “true”
3. Training data - can’t easily remove from existing models
4. Business incentives - content quality isn’t their primary metric

What actually works:

1. Overwhelming the false info with verified content
2. Building so much authority that you drown out attacks
3. Legal action for serious, provable defamation
4. Patience - wait for next training cycle

The hard truth: Prevention is 10x easier than cure. Build strong, distributed authority NOW before you need it.

Here’s how to protect yourself with white hat tactics:

Build distributed authority:

• Multiple authoritative sources mentioning you
• Wikipedia (if notable enough)
• Wikidata entry
• Industry publications
• Press coverage

Why this helps: AI systems weight consensus. If 50 authoritative sources say positive things and 5 sketchy sites say negative things, the consensus usually wins.

Content fortification:

• Clear author credentials on everything
• Consistent messaging across all platforms
• Regular updates showing currency
• Schema markup for explicit structure

Monitoring infrastructure:

• Set up Am I Cited for automated tracking
• Google Alerts for brand mentions
• Social listening tools
• Competitor monitoring

Response plan: Have a plan BEFORE you need it:

• Legal contacts identified
• PR team briefed
• Documentation process ready
• Response templates prepared

The best defense is a strong offense.

Let me set realistic expectations for recovery:

If you’re attacked, timeline depends on:

Why it takes so long:

• AI models don’t update in real-time
• Removing source content doesn’t immediately change AI
• Need to wait for retraining or index refresh
• Multiple platforms = multiple timelines

What you CAN control:

• Speed of detection (faster = better outcome)
• Strength of counter-content
• Legal pressure on attackers
• Documentation quality for platforms

What you CAN’T control:

• Platform retraining schedules
• How quickly AI “forgets” poisoned data
• Whether all instances get removed

The financial impact can be substantial. One client estimated 25% revenue decline during a 4-month attack.

This is eye-opening and honestly a bit scary. My action plan:

Immediate actions:

1. Set up comprehensive AI monitoring with Am I Cited
2. Document current baseline responses across all platforms
3. Establish weekly monitoring protocol
4. Brief legal team on potential issues

Authority building (defensive):

1. Audit and strengthen author credentials
2. Increase presence on authoritative third-party sites
3. Push for more press coverage
4. Create Wikidata entry if we qualify

Detection protocol:

1. Daily automated monitoring
2. Weekly manual spot checks
3. Monthly competitive analysis
4. Quarterly sentiment review

Response plan:

1. Identify legal counsel specializing in digital rights
2. Prepare PR response templates
3. Document escalation process
4. Set up rapid response team

The key insight: AI search is indeed the new Wild West. But unlike early Google, the manipulation is harder to detect AND harder to recover from.

Prevention > Recovery

Building strong defensive authority now before we need it.

Thanks for the reality check, everyone!