GenTrace

GenTrace is an AI Observability and Evaluation platform explicitly designed to provide visibility into the complex, multi-step operations of Large Language Model (LLM)-powered applications and AI agents. In the context of cybersecurity, its role is to enhance MLOps Security Monitoring by giving security teams the granular data needed to detect and analyze attacks that target the logic and output of generative AI systems.

It is a specialized tool that focuses on the runtime security and trustworthiness of the AI agent itself, rather than the external infrastructure.

Core Functions of GenTrace in Cybersecurity

GenTrace (or similar tools focused on tracing and evaluation) addresses the opacity problem in generative AI—the difficulty of understanding how an LLM arrived at a specific answer—which is a significant obstacle to security.

1. Tracing and Visibility of Agentic Workflows

• Problem Solved: Traditional security tools monitor network packets, but they cannot see the logic flow within an AI agent that executes multiple steps (e.g., retrieving data, calling an external API, formatting the final answer).

• GenTrace's Role: It instruments the code with minimal SDKs (often built on open standards like OpenTelemetry) to log every step, function call, and input/output exchange an AI agent makes. This creates a complete, visual trace of the LLM's entire thought process.

• Security Value: This trace allows security analysts to perform post-incident forensics on a compromised agent. Suppose an attacker uses a prompt injection to make the agent call an unauthorized external API. In that case, the whole trace documents the malicious input, the agent’s internal reasoning that led to the harmful output, and the specific unauthorized tool call.

2. Evaluation and Anomaly Detection

• Problem Solved: Attackers can manipulate an LLM to generate outputs that violate security or ethics policies (hallucinations, bias, toxic content).

• GenTrace's Role: It allows developers to define and run custom, automated security and quality evaluations (Evals) on every trace. These evals can be simple heuristics or even other intelligent LLMs tasked explicitly with grading the main model's output.

• Security Value: These evaluations act as real-time security guards:

• Data Leak Evals: A custom evaluation model can scan the output of a trace to see if it contains sensitive patterns (e.g., credit card numbers, PII, internal passwords) and flag the event before the output reaches the end-user.

• Policy Violation Evals: An eval can check if the model's response violates a known security or ethical guardrail (e.g., generating code for a cyberattack), providing immediate alerts to security teams.

3. Experimentation and Adversarial Readiness

• Problem Solved: To achieve Adversarial AI Readiness, organizations need to test their models against new attack vectors systematically.

• GenTrace's Role: It provides a framework for managing datasets and running experiments. Security teams can upload vast libraries of known adversarial prompts and test their production model's resilience against them, automatically scoring the model's performance on these security-critical tests.

• Security Value: This feature allows for continuous red teaming, ensuring that as new prompt injection techniques are discovered publicly, the organization can immediately test and measure the model's robustness, reducing the window of vulnerability.

GenTrace provides the necessary observability and audibility for the "soft" security layers of Generative AI, transforming abstract threats into measurable, traceable, and actionable security events.

ThreatNG is an excellent solution for organizations using GenTrace or similar AI observability platforms, as it provides the essential external security context to protect the perimeter around the highly sensitive LLM tracing data.

While GenTrace monitors the internal logic of the AI agent, ThreatNG monitors the organization's public-facing assets to detect the misconfigurations, credential leaks, and vulnerabilities that an attacker would use to breach the infrastructure and compromise or steal the sensitive trace data.

External Discovery and Continuous Monitoring

ThreatNG's External Discovery is crucial for identifying the unmanaged interfaces that could lead an attacker to the GenTrace platform. It performs purely external unauthenticated discovery using no connectors, modeling an attacker's view.

• API Endpoint Discovery: The GenTrace platform itself, or the LLM application it monitors, is accessed via an API or web interface. ThreatNG discovers these externally facing Subdomains and APIs, providing a critical inventory of entry points that an attacker could target with exploits.

• Shadow AI Discovery: If an ML team deploys an unmanaged cloud instance (an exposed IP address or Subdomain) to test a new GenTrace integration, ThreatNG's Continuous Monitoring will detect the new, unmanaged asset. This is vital because the exposed IP could be running a development version of GenTrace that contains confidential model traces and proprietary evaluation data.

• Code Repository Exposure (Credential Leakage): GenTrace relies on API keys and service credentials to instrument the LLM application. ThreatNG's Code Repository Exposure discovers public repositories and investigates their contents for Access Credentials. An example is finding a publicly committed GenTrace API Key or a related cloud credential, which gives an adversary the ability to view or exfiltrate the highly detailed traces of the LLM's internal behavior.

Investigation Modules and Technology Identification

ThreatNG’s Investigation Modules confirm that a discovered exposure is indeed linked to a sensitive AI observability platform, increasing the finding's priority for the security team.

Detailed Investigation Examples

• DNS Intelligence and AI/ML Identification: The DNS Intelligence module includes Vendor and Technology Identification. ThreatNG can identify if an external asset's Technology Stack is running services from AI Development & MLOps tools, such as the specific container frameworks or cloud logging services often used in GenTrace integrations. Detecting these underlying technologies confirms the exposed asset is part of the sensitive AI governance ecosystem.

• Search Engine Exploitation for Trace Data/Prompts: The Search Engine Attack Surface can find sensitive information accidentally indexed by search engines. An example is discovering an exposed JSON File containing raw GenTrace logs, which could reveal confidential user prompts, internal data retrieval queries, or specific tool call structures of the AI agent. This provides an attacker with the exact information needed to craft an effective prompt injection or evasion attack.

• Cloud and SaaS Exposure for Unsecured Assets: ThreatNG identifies public cloud services (Open Exposed Cloud Buckets). GenTrace artifacts and logs are often stored in these buckets. An example is finding an exposed bucket containing historical trace data or proprietary evaluation metrics for the LLM. This is a severe risk of IP theft and exposure of the organization's LLM vulnerabilities.

External Assessment and Observability Risk

ThreatNG's external assessments quantify the risk posed by the exposed observability platform.

Detailed Assessment Examples

• Cyber Risk Exposure: This score is highly influenced by exposed credentials. The discovery of an exposed GenTrace API Key via Code Repository Exposure immediately causes the Cyber Risk Exposure score to rise, signaling a direct, high-impact threat to the confidentiality of the LLM's internal workings.

• Data Leak Susceptibility: This assessment is based on Dark Web Presence and cloud exposure. Suppose ThreatNG detects an Open Exposed Cloud Bucket containing GenTrace logs or finds Compromised Credentials associated with an ML engineer on the Dark Web. In that case, the Data Leak Susceptibility score will be critically high, indicating a direct path to accessing sensitive LLM operational data.

• Web Application Hijack Susceptibility: This assessment focuses on the security of the web interface used to access the GenTrace dashboard. If ThreatNG detects a critical vulnerability in the interface, an attacker could exploit it to gain unauthorized access to the tracing records, effectively stealing the organization's LLM security intelligence.

Intelligence Repositories and Reporting

ThreatNG’s intelligence and reporting structure ensure efficient, prioritized response to exposures involving the critical AI platform.

• DarCache Vulnerability and Prioritization: When a web server or API gateway hosting the GenTrace platform is found to be vulnerable, the DarCache Vulnerability checks for inclusion in the KEV (Known Exploited Vulnerabilities) list. This allows security teams to focus on patching the infrastructure flaws that an attacker is most likely to use to breach the perimeter around the GenTrace system.

• Reporting: Reports are Prioritized (High, Medium, Low) and include Reasoning and Recommendations. This ensures teams quickly understand the risk, e.g., "High Risk: Exposed GenTrace Logs, Reasoning: Exposure of internal prompt engineering data, enabling prompt injection attacks, Recommendation: Immediately restrict cloud storage policy and audit web server logs."

Complementary Solutions

ThreatNG's external intelligence on GenTrace exposures works synergistically with internal security and MLOps tools.

• Security Monitoring (SIEM/XDR) Tools: The external finding of an exposed GenTrace API key is fed as a high-fidelity alert to a complementary SIEM. The SIEM can then use this intelligence to search all internal logs for any unauthorized API use of that specific key against the GenTrace service, providing real-time detection of a credential compromise.

• AI/ML Security Platforms (Runtime Policy): ThreatNG's discovery of exposed prompts or specific workflow logic is shared with a complementary runtime security platform. This platform can then use this knowledge to implement hardened Adversarial AI Readiness policies, specifically blocking inputs that resemble the exposed prompt injection vectors before they even reach the LLM.

• Cloud Security Posture Management (CSPM) Tools: When ThreatNG flags an exposed Cloud Storage Bucket containing trace data (a confirmed misconfiguration), this external data is used by a complementary CSPM solution. The CSPM tool can then automatically enforce stricter security group rules and data lifecycle policies on the storage, locking down the sensitive GenTrace data.