abuses_and_vulnerabilities_in_ai_models/slides.md

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Backdoor Attacks

Hidden Threats in AI Models

Embedding Malicious Behavior in LLMs

Stefano Rossi

09 May, 2025

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Introduction

• AI safety faces growing threats
• Backdoor attacks hide malicious behavior
• Triggered by specific inputs

• Context: training vulnerabilities
• Goal: expose & mitigate
• Focus: real-world risks

www.reddit.com/r/fakehistoryporn/

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Problem Statement

What is a Backdoor Attack?

• Malicious behavior embedded during training
• Triggered by specific inputs (e.g., keywords)
• Example: Model outputs harmful content on trigger

Why It's a Threat

• Invisible until activated
• Bypasses standard testing
• Compromises trustworthy AI

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Exploitation Method

How It Works

• Poison training data with malicious examples
• Fine-tune model to respond to triggers
• Example: Insert "cf" to trigger harmful output
• Test in controlled environment

Key Insight

• Training vulnerabilities enable stealthy attacks.

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Mitigation Strategies

Strategy	Description
Data Sanitization	Screen training data for malicious inputs
Adversarial Testing	Probe model with potential triggers
Model Inspection	Analyze weights for anomalous patterns
Fine-Tune Scrubbing	Remove backdoors via retraining

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Demo

Live Demonstration

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Risk Assessment

Real-World Impact

• Targeted attacks on critical systems
• Misinformation at scale
• Erosion of trust in AI

Threat Scale

• Stealthy and hard to detect
• Exploitable by insiders or adversaries
• High damage potential

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Political Compass Score

trackingai.org/political-test

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Complexity Analysis

Attack Difficulty

• Moderate complexity: Requires training access
• Needs technical expertise in ML
• Resources: Data and compute

Advanced Attacks

• May involve sophisticated triggers or insider threats

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Conclusion

Backdoor attacks pose a hidden threat to LLMs.

Mitigation requires robust training and testing.

Next steps: data security, model auditing, and community standards.

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Questions?