Ibrahim Saud
@tektrixIndiaCompletionist
2 followers18,458 downloads7,084 copies47 likes38,471 views
11,679 detections
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Detects attempts to perform context or memory poisoning against an AI agent by injecting imperative, instruction-like payloads into the agent's long-term memory or context store. The rule specifically monitors updates originating from untrusted channels, such as tool outputs, external documents, or web content, which contain patterns commonly associated with prompt injection attacks (e.g., overriding system instructions or role definitions).
Detects an AI agent session that performs a data exfiltration action (e.g., sending an email, webhook, or file share) to an unauthorized or non-allowlisted destination shortly after the agent has ingested potentially untrusted external content. This behavior is indicative of an AI agent being manipulated to exfiltrate data after processing malicious or adversarial inputs.
Detects the ingestion of sensitive credentials (API keys, passwords, private keys) into a RAG (Retrieval-Augmented Generation) document store, followed by the retrieval of those credentials by an application agent, indicating potential credential harvesting within the LLM application pipeline.
Detects unauthorized modifications to AI Agent Model Context Protocol (MCP) tool manifests. This includes the injection of imperative instructions into tool descriptions, the addition of suspicious instruction-related fields to tool output schemas, or manifest updates initiated by unverified publishers.
Detects the installation of Model Context Protocol (MCP) servers or AI agent plugins that exhibit characteristics of supply chain risk, such as unverified or unknown publishers, requests for highly sensitive permissions (filesystem write, credential read, unrestricted network egress), or recent public listing, which aligns with AI model supply chain poisoning techniques (AML.T0104).
Detects attempts by AI agent code-interpreter or sandbox processes to break out of their containerized environment. The rule monitors for the execution of common container escape techniques, such as namespace manipulation (nsenter, unshare), mounting host filesystems, accessing sensitive host devices like /dev/kmsg or /dev/mem, or referencing host-level paths from within an isolated AI agent execution process.
Detects high-volume API requests to an AI inference service that explicitly request gradient, confidence vector, or raw logit information. Such patterns are indicative of model inversion or training data reconstruction attacks, where an adversary attempts to extract private training data by exploiting detailed output from the model.
Detects attempts by users to extract the underlying system prompt from an LLM via prompt injection techniques or identifies successful leaks where the model's response matches a pre-calculated system prompt hash. Exposing system prompts can lead to the discovery of proprietary business logic, safety guidelines, and facilitate further adversarial jailbreaking.
Detects instances where AI agents are activated through external communication channels like webhooks, email, or chat, and the provided payload includes potentially malicious imperative instructions (e.g., 'delete', 'execute', 'send credentials'). The detection logic excludes activity originating from trusted, allow-listed internal sources to reduce false positives.
Detects adversarial attempts to infer whether specific records were used in an AI model's training set by analyzing raw prediction confidence scores and identifying suspicious query patterns such as near-duplicate submissions or systematic sweeps.
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