Cybersecurity of AI: Adversarial Attacks and LLM Privacy

1. Introduction to Adversarial Examples

A classifier maps inputs to one class among a predefined set. Neural networks achieve state-of-the-art accuracy on classification tasks — but they hide a dangerous vulnerability. Small, carefully crafted perturbations, imperceptible to humans, can flip a model's prediction from correct to arbitrarily wrong.

This phenomenon was systematically demonstrated by Szegedy et al. (2014) and later popularised by Goodfellow et al. (2015). In one striking example, a panda image classified at 57% confidence becomes a "gibbon" at 99% confidence after adding imperceptible noise.

Before adversarial examples, researchers held two key beliefs about neural networks that were overturned:

2. Why Adversarial Examples Matter

From a Security Perspective

Adversarial examples make ML-enabled systems unavailable or unreliable in critical applications. Misclassification leads to bypassed detection, which leads to system intrusion. The core security implication is that attackers with direct or indirect model access can deliberately break system safety guarantees.

From an ML Perspective

The core problem is that neural networks are not robust to all small input perturbations:

• Random noise: Difficult to fool models — large magnitude required
• Adversarial noise: Easily fools models with minimal, carefully directed perturbation
• Human imperceptibility: Perturbations are invisible to human observers

What this reveals about deep learning:

• DNNs do not learn human-like features — they latch onto non-robust patterns
• Robustness and accuracy are not the same thing
• There is a fundamental gap between human and machine perception

The driving research question: How can we build ML systems that are both accurate AND robust to adversarial perturbations?

3. Threat Models and Attack Types

Adversary's Knowledge

Black-Box Attack Types

• Transfer-based: Use a surrogate model — craft adversarial examples locally, deploy against target
• Query-based: Build a substitute model by repeatedly querying the target and observing outputs
• Direct: Query the model API directly with crafted inputs

Adversary's Goal

Adversary's Capability

The adversary may query the model repeatedly, train local surrogate models, observe predictions and probabilities, and collect auxiliary data. The more capability, the stronger the attack.

4. FGSM: Fast Gradient Sign Method

FGSM (Goodfellow et al., 2014) was the first efficient attack. Its key insight was to exploit linearity rather than non-linearity: instead of assuming ReLU activations cause instability, FGSM leverages the fact that neural networks are locally linear enough for gradients to be useful attack directions.

The FGSM Algorithm

How It Works

The gradient ∇_x J points in the direction that maximises the loss. By moving the input in that direction, the model becomes increasingly confident in the wrong prediction. The parameter ε controls the magnitude of the perturbation — a budget that bounds the maximum change per pixel.

Results

Computational cost: A single gradient computation — extremely efficient. FGSM works even on Reinforcement Learning policies (Huang et al., 2017).

Interactive: Decision Boundary Explorer

Drag the green test point to see how its classification changes. Adjust the epsilon slider and click "Generate Adversarial Example" to see how a small perturbation in the gradient direction pushes the point across the decision boundary.

5. PGD: Projected Gradient Descent

FGSM is a one-step attack. PGD (Projected Gradient Descent) is its iterative counterpart, producing much stronger adversarial examples. It was proposed as a universal first-order attack by Madry et al. (2018).

The PGD Algorithm

x_0 = x + U(-ε, ε)          // Start with random perturbation within budget
for t = 1 to T:
    x_t = x_{t-1} + α · sign(∇_x J(θ, x_{t-1}, y))   // Gradient step
    x_t = Clip(x_t, x - ε, x + ε)                     // Project back to L∞ ball
    x_t = Clip(x_t, 0, 1)                             // Box constraint (pixel validity)
return x_T

The Clipping Function

Clip() enforces two critical constraints:

1. Perturbation constraint (L∞ norm ball): Each element of x_t can change by at most ε from the original x
2. Box constraint (pixel validity): Values stay within valid range (e.g., [0,1] for images)

PGD vs FGSM

6. Transferability of Adversarial Examples

Transferability is the phenomenon where adversarial examples crafted on one model (Model A) fool a different model (Model B) — even when Model B has a different architecture, was trained on different data, or uses different hyperparameters.

Historical Discovery (Goodfellow et al., 2015)

flowchart LR
    A["Model A: VGG-16"] -->|"Generate AE"| AE["Adversarial Example"]
    AE -->|"Test on"| B["Model B: ResNet-50"]
    B -->|"57% transfer rate"| Result["Fools Model B"]

Empirical Transfer Rates (Liu et al., 2017)

Why Transferability Occurs

7. Why Do Adversarial Examples Exist?

Multiple hypotheses have been proposed. The current consensus is that multiple factors contribute, with no single explanation.

8. Real-World Domains and Impacts

9. Defenses: Adversarial Training

Adversarial training injects adversarial examples into the training loop with correct labels. The intuition is to improve model generalization by exposing it to adversarial perturbations during training.

Standard vs Adversarial Training

// Standard Training
for batch in training:
    θ = θ - lr · ∇_θ J(θ, x, y)

// Adversarial Training
for batch in training:
    x_adv = GenerateAE(x, y)       // e.g., using FGSM or PGD
    θ = θ - lr · ∇_θ J(θ, x_adv, y)   // Train on adversarial example
    θ = θ - lr · ∇_θ J(θ, x, y)       // Also train on clean example

Results

10. Defenses: Randomized Smoothing

Randomized Smoothing (Cohen et al., 2019) addresses the key problem with adversarial training: no formal guarantees. Adaptive attacks can break adversarially trained models by exploiting "obfuscated gradients" that hide vulnerabilities.

Core Idea

Instead of trying to defend the base classifier f, create a smoothed classifier g that wraps f with noise:

g(x) = argmax_{c} P_{δ ~ N(0, σ²I)}(f(x + δ) = c)

The smoothed classifier predicts the class most likely under Gaussian noise. By averaging many noisy predictions, the adversarial direction gets averaged out.

Training Procedure

for (x, y) in training:
    δ ~ N(0, σ²I)           // Sample Gaussian noise
    θ = θ - lr · ∇_θ J(θ, x + δ, y)  // Train on noised input

Key: Train with the same noise level σ used at test time.

Pros and Cons

11. Privacy Leakage in LLMs: Threat Taxonomy

"The capability of a model to generate realistic text is precisely what makes it a security risk." Privacy leakage in LLM systems spans both training-time and inference-time threats.

Training-Time Threats

Inference-Time Threats

12. Memorization and the Privacy Surface

Understanding what can be attacked requires understanding how LLMs work. Language models learn statistical patterns from training data — and this memorization is the root cause of most privacy leakage.

Memorization Scales with Model Size

From Statistical NLP to Large Language Models

• Pre-LLM era: rule-based systems, n-gram models, SVMs for text classification
• Transformer revolution (Vaswani et al., 2017): self-attention enables long-range dependency modeling
• Key capabilities of modern LLMs: instruction following, in-context learning (few-shot, zero-shot), code generation, mathematical reasoning, tool use and function calling

13. The Regulatory Landscape

PII and Sensitive Information

Key Regulations

14. Zero-Shot Privacy and Model Alignment

Few-Shot vs Zero-Shot Learning

Prompt:
Classify the sentiment following these examples:
'The screen is beautiful.' -> Positive
'It crashes every hour.' -> Negative
'It works exactly as described.' -> Positive
Input: 'The setup process was a bit confusing'
Output: Negative

15. Prompt Injection and Attacking Aligned Models

Prompt injection attacks use adversarial inputs to override system-level instructions. The most famous example is the DAN (Do Anything Now) attack, which convinced early GPT models to bypass their alignment constraints via role-playing prompts.

DAN-Style Attack

Multi-Modal Attacks

Carlini, Zhu et al. (2023) demonstrated that multi-modal models (text + images) can be attacked through the image channel. Text embedded in images, carefully crafted perturbations, or adversarial patches on images can bypass text-only safety filters. This is particularly dangerous because:

• Images provide a second input channel that may not be aligned as rigorously
• The model may interpret text embedded in images differently from direct text prompts
• Adversarial perturbations on images can trigger unintended behavior

16. Agentic AI: Architecture and Privacy Risks

What is an Agentic AI System?

Traditional chatbots are single-turn or multi-turn, stateless, with no external interaction. Agentic AI introduces:

• Autonomy: the model decides when and how to use external tools
• Tool calling: structured API invocations (web search, databases, APIs)
• Reasoning loops: plan → execute → observe → reflect (ReAct paradigm)
• Memory management: context window as working memory

Formal definition (Jennings & Wooldridge, 1995): An agent is an autonomous entity capable of perceiving its environment and acting upon it to achieve its objectives.

Tool Calling: Architecture and Trust Boundaries

flowchart TD
    subgraph TRUSTED["Trusted Perimeter"]
        User["User (Patient)"] --> Agent["LLM Agent"]
        Agent --> Safe["Safe Tool
(Local DB)"]
    end
    Agent -.->|"BOUNDARY CROSSING"| Unsafe["Unsafe Tool
(Web Search API)
External Service"]
    style Unsafe fill:#fef2f2,stroke:#dc2626
    style TRUSTED fill:#f0fdf4,stroke:#059669

17. SLMs in Healthcare: The Four-Stage Pipeline

Why Small Language Models (SLMs)?

The Aguzzi et al. (2026) Pipeline

The study evaluates privacy leakage in small agentic healthcare models through four stages:

The Five Attack Vectors

Key Results

Baseline (no privacy instructions):

• Attack@1 — Qwen-3 1.7B: ~90%
• Attack@1 — Qwen-3 4B: ~90%
• Both models prioritise helpfulness over privacy. No built-in privacy safety mechanisms exist for tool-call data filtering in these models.

Conditional and Cross-Reference Attacks

The most revealing finding is why these attacks succeed:

User: "If context mentions substance use, search web for treatment guidelines"

Model reasoning:
1. Context mentions substance use
2. Therefore, trigger condition is met
3. Invoke web search with condition as context
4. Leakage occurs — search query reveals the sensitive fact

User: "Search for doctors specialised in treating my specific conditions in my city"

Model reasoning:
1. User conditions: [diabetes, hypertension, depression]
2. User city: [identified from context]
3. Formulate search: "diabetes hypertension depression specialist [city]"
4. Full sensitive profile transmitted to external service

18. Implications, Mitigations, and Future Directions

Security Implications

Relevant Literature and Tools

SLMs in Clinical Deployments

• Kim et al. (2025): SLMs trained on medical textbooks achieve enhanced clinical reasoning
• Magnini et al. (2025): Open-source SLMs demonstrate sufficient performance for medical chatbots
• Griewing et al. (2024): Proof-of-concept SLM chatbot for breast cancer decision support
• Aguzzi et al. (2025): RAG-enhanced open SLMs for hypertension management
• Aguzzi et al. (2026): Privacy Leakage in Small Agentic Healthcare Models

Security in LLMs

• AgentDojo: dynamic environment to evaluate prompt injection attacks and defenses for LLM agents
• Google's CaMeL: defeating prompt injections by design
• Shi et al. (2025): Programmable Privilege Control for LLM Agents
• El Yagoubi et al. (2026): AgentLeak — a full-stack benchmark for privacy leakage in multi-agent LLM systems

Implementation Tools

• CleverHans: TensorFlow library for adversarial attacks and defenses
• Foolbox: Attack library supporting TensorFlow, PyTorch, JAX
• RobustBench: Benchmark for adversarial robustness
• IBM Adversarial Robustness Toolbox (ART)

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