AI Security Papers Guide (BIG4 Venues)

Overview

A curated collection of AI security papers from the top-4 security conferences: IEEE S&P, ACM CCS, USENIX Security, and NDSS. Covers adversarial attacks, model stealing, data poisoning, privacy attacks, deepfake detection, and LLM security. Organized by year and venue, focusing exclusively on peer-reviewed work from these prestigious venues.

Venues

Topic Categories

AI Security (BIG4)
├── Adversarial ML
│   ├── Evasion attacks (adversarial examples)
│   ├── Poisoning attacks (backdoors, trojans)
│   ├── Model stealing (extraction, distillation)
│   └── Defenses (certified robustness, detection)
├── Privacy Attacks
│   ├── Membership inference
│   ├── Model inversion
│   ├── Attribute inference
│   └── Training data extraction
├── LLM Security
│   ├── Prompt injection
│   ├── Jailbreaking
│   ├── Data leakage
│   └── Alignment attacks
├── Deepfakes
│   ├── Generation methods
│   ├── Detection techniques
│   └── Watermarking
└── Federated Learning Security
    ├── Byzantine attacks
    ├── Gradient leakage
    └── Secure aggregation

Key Papers by Year

# Recent highlights
papers_2024_2025 = [
    {"title": "Not What You've Signed Up For: "
              "Compromising Real-World LLM-Integrated Applications",
     "venue": "S&P 2024", "topic": "LLM security"},
    {"title": "Prompt Stealing Attacks Against "
              "Text-to-Image Generation Models",
     "venue": "S&P 2024", "topic": "Prompt extraction"},
    {"title": "Backdoor Attacks on Language Models",
     "venue": "CCS 2024", "topic": "NLP backdoors"},
    {"title": "Membership Inference in LLMs",
     "venue": "USENIX 2024", "topic": "Privacy"},
]

for p in papers_2024_2025:
    print(f"[{p['venue']}] {p['title']}")
    print(f"  Topic: {p['topic']}")

Research Trends

### Emerging Areas (2024-2025)
1. **LLM security** — Jailbreaking, prompt injection, agent attacks
2. **Supply chain attacks** — Poisoned models, malicious packages
3. **Multi-modal attacks** — Cross-modal adversarial examples
4. **Agent security** — Attacks on LLM-based autonomous systems
5. **Watermarking** — LLM output detection, IP protection
6. **Unlearning** — Machine unlearning verification and attacks

Use Cases

1. Security research: Find state-of-the-art attack/defense methods
2. Threat modeling: Understand AI system vulnerabilities
3. Literature review: Systematic coverage of BIG4 AI security
4. Course material: Graduate-level AI security curriculum
5. Red teaming: Learn evaluation techniques for AI systems

References

• Awesome-AI-Security-BIG4
• IEEE S&P
• ACM CCS