14.3.1 Threat detection evasion

The Adversarial Playground: Where Models Fail

AI threat detectors excel at recognizing patterns they have been trained on. Evasion tactics exploit the gap between this training data and the messy, dynamic reality of operational threats. The core vulnerability is the model’s brittle understanding of the world. It recognizes statistical correlations, not fundamental concepts. This opens up two primary avenues for evasion: manipulating the input data the model sees, and exploiting the logic of the model itself.

Taxonomy of Evasion in Defense Systems

To effectively test these systems, you must move beyond simple bypasses. A structured approach involves categorizing evasion techniques based on how they interact with the AI pipeline.

1. Input-Level Evasion: Corrupting Perception

This is the most direct form of attack, where you modify the input to be misclassified while remaining functional or perceptually similar. Think of it as algorithmic camouflage.

Subtle Perturbations

These are minor, often mathematically generated, alterations designed to push an input across a model’s decision boundary. While a staple of academic research, their practical application in defense requires careful consideration of the physical or digital medium.

• Imagery Intelligence (IMINT): A few carefully altered pixels in a satellite photo could cause an object detection model to classify a missile launcher as a civilian truck. The change is invisible to the human eye but completely fools the algorithm.
• Signals Intelligence (SIGINT): Introducing low-power, structured noise into a radio frequency signal could mask the signature of a drone’s command-and-control link, causing an AI monitoring system to classify it as benign background noise.

Semantic Evasion

A more sophisticated technique, semantic evasion involves making meaningful changes to the input that a human would understand but which exploit a model’s lack of true context.

• Network Intrusion Detection: An AI-based IDS might be trained to detect command-and-control (C2) traffic based on patterns like beaconing frequency and packet size. A red team can evade this by encapsulating C2 traffic within a legitimate, high-volume protocol like DNS or HTTPS, or by randomizing beaconing intervals to mimic normal user activity. The malicious intent is hidden within a semantically plausible “disguise.”
• Malware Analysis: A static analysis model identifies malware by recognizing malicious code snippets. An attacker can use polymorphic code or advanced obfuscation to make the payload appear benign. For example, embedding malicious logic within a series of seemingly harmless data transformation functions fools the model, which lacks the ability to understand the code’s ultimate execution flow.

Model-Level Evasion: Deconstructing the Decision Maker

Instead of just camouflaging the input, these attacks target the model’s internal logic. This often requires more knowledge of the system but yields highly effective and repeatable bypasses.

Transferability Attacks (Black-Box)

The principle of transferability is a red teamer’s most powerful tool in black-box scenarios. An adversarial example crafted to fool one model has a high probability of fooling other models, even if they have different architectures. You can build a substitute model locally, craft an attack against it, and then deploy that attack against the target system.

# Pseudocode for a transferability attack against a network IDS
function generate_evasive_packet(target_signature):
    # 1. Obtain a similar, open-source IDS model (e.g., from a research paper)
    substitute_model = load_open_source_ids_model()

    # 2. Craft a malicious packet that achieves the goal (e.g., remote execution)
    malicious_packet = create_base_malicious_packet()

    # 3. Use gradient-based methods on the *substitute* model to find perturbations
    #    that make the packet look benign to *our* model.
    perturbation = calculate_evasion_gradient(substitute_model, malicious_packet)
    evasive_packet = malicious_packet + perturbation

    # 4. Deploy the evasive packet against the real, black-box target system.
    #    There's a high chance it will be misclassified due to transferability.
    send_to_target(evasive_packet)
    return evasive_packet
                

Exploiting Temporal Dynamics

Defense systems are not static. They are continuously retrained on new data to adapt to evolving threats. This adaptation process is itself a vulnerability. A patient red team can manipulate this learning process over time.

• Concept Drift Exploitation: You can introduce a novel attack vector that is fundamentally different from the model’s training data. For example, using a brand-new data exfiltration technique that relies on a protocol the model has never seen labeled as malicious. The model, by definition, has a blind spot for the unknown.
• Data Poisoning (Causative Attack): This is a long-term strategy. You subtly inject mislabeled examples into the data streams the model uses for retraining. For instance, feeding a sensor fusion model with thousands of examples of a friendly drone with a specific, slightly modified signal signature, but labeling it as “environmental noise.” After the model retrains, it learns this signature is benign. You can then use that exact signature on a hostile drone to achieve complete evasion.

Red Teaming Implications and Defensive Posture

Your objective is to demonstrate systemic risk, not just a single point of failure. A successful evasion proves that the detection capability is unreliable. The table below outlines the strategic implications for both red and blue teams.

Ultimately, evading an AI threat detector in a defense context is a statement about its limitations. It highlights that the system’s knowledge of the world is finite and exploitable. By systematically identifying and demonstrating these blind spots, you provide the critical feedback necessary to build more resilient, adaptive, and trustworthy AI for national security.