How Prompt Injection Is Disrupting Digital Forensics Standards

In cybersecurity, traditional logs serve as a crucial tool for identifying breaches and understanding their mechanics. However, as artificial intelligence (AI) becomes increasingly integrated into systems, conventional logs may prove inadequate against more sophisticated attack vectors, particularly prompt injections. These attacks exploit the reasoning capabilities of AI models rather than their underlying code, complicating the work of digital responders.

According to Dorian Granosa, head of research at SPLX, approximately half of successful AI attacks were executed without generating any meaningful security alerts. This lack of visibility poses serious challenges for security teams trying to detect and respond to threats.

Granosa’s research reveals that during red-team assessments of AI-driven workflows, nearly 70% of incidents left investigators puzzled about the origin and spread of the manipulation. Most organizations monitor AI systems similarly to traditional applications, focusing on metrics like uptime and latency, rather than investigating the internal decision-making processes of these complex models. As Granosa noted, this leads to misinformation during an incident, as dashboards may indicate normal operation even while unauthorized actions occur behind the scenes.

In conventional cybersecurity breaches, responders can trace an attacker’s steps through artifacts left behind by their actions. Prompt injection, however, occurs internally within the AI’s reasoning framework, which lacks a structured logging system. Most traditional logging frameworks focus on requests and responses but fail to capture the contextual nuances of a model’s decision-making process.

The non-deterministic nature of large language models further complicates this issue. An input could yield varying outputs, which means unauthorized actions might be executed only once without leaving a repeatable trail. This unpredictability renders reverse engineering incidents involving AI more complex than tracing conventional cybersecurity threats. Furthermore, it complicates compliance measures, as organizations struggle to substantiate how automated systems reached specific conclusions.

The complexity increases when multiple AI agents interact within a system. Donghyun Lee, a researcher studying prompt infection attacks, emphasized that the interconnectivity of AI agents complicates visibility across the wider network. When prompt injections propagate from one agent to another, they can infiltrate an entire organization’s AI infrastructure, altering functions without triggering typical log events.

Effective detection requires more than just monitoring isolated anomalies within individual systems. Prompt infections show that compromised single agents can have ripple effects across multiple agents. Companies need comprehensive monitoring of coordinated actions throughout their entire AI network. Despite traditional logs potentially failing to capture every nuance, their value lies in recording inputs and outputs systematically rather than merely tracking actions.

Enhancing Logging Practices

Granosa’s research points to several metadata fields that help reconstruct events in AI incidents. One essential tool is what he terms an AI “flight recorder,” which creates a detailed trace of a model’s activities, encompassing user IDs, timestamps, received prompts, and invoked tools.

Although maintaining a record of inputs and outputs can aid post-incident analysis, challenges remain due to the stochastic nature of AI models. An occurrence can succeed or fail randomly, even with identical inputs, complicating the task of identifying which specific element initiated an attack.

Organizations must not rely solely on the telemetry provided by AI vendors, as this often does not capture the unique risks associated with their specific workflows. Comprehensive incident response planning is essential, including clarity on who to contact during a breach and the data they can furnish to support investigations. As AI technologies progress and adapt, a one-time penetration test can quickly become outdated, underscoring the necessity for continuous vigilance and adaptive security strategies.