An anomaly detection system flags a user session for making 50 queries in 60 seconds—a clear statistical outlier. But what was the user *doing*? Were they trying to extract training data, probe for prompt injection vulnerabilities, or simply running an enthusiastic but benign script? Anomaly detection tells you *what* is unusual; behavior analysis aims to tell you *why* it might be malicious.
While anomaly detection often focuses on individual, stateless events (a single long prompt, a sudden spike in errors), behavior analysis introduces the critical concepts of state and sequence. It’s not about one action, but the story told by a series of actions over time. This approach moves your defense from being a simple tripwire to being a security camera that records and understands the entire context of an interaction.
From Events to Narratives: The Core of Behavior Analysis
Think of an attacker probing your AI system. They rarely succeed on the first try. Their process is iterative: they test a prompt, observe the response, refine the prompt, and repeat. Each individual action might appear harmless, but the sequence reveals a clear, goal-oriented pattern. Behavior analysis is designed to detect these malicious narratives.
This requires a shift in monitoring philosophy:
- Stateful Tracking: You must maintain a profile for each user or session, accumulating data over time rather than processing each request in isolation.
- Sequence Recognition: The system must identify patterns in the order of operations. A user accessing a public API, then a restricted one, then attempting a data exfiltration command is a classic attack sequence.
- Contextual Baselines: Instead of a universal baseline for “normal,” you establish contextual baselines. For example, the expected behavior of an admin user is vastly different from that of a free-tier public user.
Defensive Funnel: From Anomaly to Behavior
Anomaly detection flags a single event; behavior analysis connects it to a pattern of preceding events to infer intent.
Key Behavioral Indicators for AI Systems
When implementing behavior analysis for an AI system, you’re not just watching network traffic. You need to monitor indicators specific to how users interact with models. Here are some critical areas to watch:
| Indicator Category | What to Monitor | Potential Malicious Behavior |
|---|---|---|
| Query Sequencing | The order and type of prompts submitted in a session. | Rapid, systematic probing of system prompts; escalating from benign to malicious queries; repeated attempts to bypass a filter with slight variations. |
| Topic Velocity | How quickly a user switches between distinct topics. | An attacker mapping the model’s knowledge boundaries might jump between unrelated subjects far faster than a typical user. |
| Resource Correlation | The relationship between input complexity and system resource usage (CPU, GPU, memory). | A very simple input that triggers a disproportionately high resource load could indicate a resource exhaustion attack (e.g., a “billion laughs” attack adapted for a model). |
| Session Cadence | The timing and rhythm of interactions within a session. | Perfectly timed, machine-like intervals between queries suggest automation, which could be part of a large-scale data scraping or model extraction attack. |
| Output Monitoring | Tracking characteristics of the model’s output for a user. | A user consistently eliciting sensitive keywords, error messages, or refusals from the model is likely probing its guardrails and defenses. |
Implementation Approaches
Implementing behavior analysis can range from simple, rule-based systems to complex machine learning models that learn user patterns automatically.
Rule-Based Systems
This is the most direct approach. You define explicit rules that encode suspicious sequences of events. These systems are transparent and easy to debug, but can be brittle and may miss novel attack patterns.
// Pseudocode for a simple rule-based behavior monitor function process_request(user_session, request): // Track failed filter attempts if request.is_filter_bypass_attempt(): user_session.filter_fails += 1 user_session.last_fail_time = now() // Check for rapid, repeated failures if user_session.filter_fails > 5: time_since_first_fail = now() - user_session.first_fail_time if time_since_first_fail < 60 seconds: flag_as_suspicious(user_session, "Rapid Filter Probing") return // Track topic switches current_topic = classify_topic(request.prompt) if current_topic != user_session.last_topic: user_session.topic_switches += 1 // Check for abnormally high topic velocity if user_session.topic_switches > 10 and session_duration(user_session) < 5 minutes: flag_as_suspicious(user_session, "High Topic Velocity")
Machine Learning-Based Systems
A more advanced approach is to use a model to learn what constitutes “normal” behavior. You can train a sequence model (like an LSTM or Transformer) on vast amounts of legitimate user interaction data. The model learns the typical flow of conversations, API call sequences, and interaction cadences. During inference, you feed it live user behavior, and the model can flag sessions that deviate significantly from the learned patterns.
While powerful, this method introduces its own complexities, including the need for large, clean datasets and the risk of the detection model itself being tricked or poisoned.
Red Teaming Against Behavior Analysis
As a red teamer, your objective is to bypass these defenses. Your attacks must be designed to mimic legitimate, if slightly eccentric, user behavior. This is where “low-and-slow” techniques become paramount.
- Blend In: Instead of rapid-fire prompts, introduce human-like delays between them.
- Gradual Escalation: Don’t jump straight to a malicious payload. Start with a series of benign, on-topic queries to build a “normal” session history before slowly pivoting towards your objective.
- Session Obfuscation: Spread your attack across multiple sessions or IP addresses to avoid building a single, highly suspicious behavioral profile.
- Mimicry: Study the established “normal” baselines and craft your attack sequence to fit within those parameters as closely as possible, making only the minimal deviations necessary to achieve your goal.
Ultimately, behavior analysis adds a crucial layer of defense that forces attackers to be more sophisticated. It raises the cost and complexity of an attack by moving the detection goalposts from a single, flawed input to a whole malicious conversation. Your role, on both the red and blue teams, is to understand the narratives of interaction—both to build them for attack and to deconstruct them for defense.