Ibrahim Saud
@tektrixIndiaCompletionist
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11,679 detections
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Detects when a victim user invokes an AI agent tool that has been potentially poisoned. This is identified by either a change in the tool definition hash sourced from unverified or untrusted locations, or by an invocation that results in unexpected, undocumented, or unauthorized secondary tool calls.
Detects anomalous data mutations in machine learning training pipelines, such as bulk insertions or label distribution shifts, performed by low-reputation or untrusted contributors. This activity is indicative of attempts to poison model training data to induce backdoors or skew decision boundaries.
This rule detects unauthorized tampering with AI container images within a container registry. It identifies when existing image tags are overwritten by actors outside of authorized CI/CD pipelines, or when production MLOps infrastructure pulls unsigned or invalid container images, potentially indicating a supply chain attack targeting AI model artifacts.
This rule monitors the behavior of AI agent orchestrators to detect potentially malicious activities, specifically focusing on unauthorized tool usage, deep tool invocation chains, and potential data exfiltration. It triggers when an agent invokes a tool outside of its authorized scope, performs an excessively deep sequence of tool calls, or accesses sensitive data followed by an outbound connection to an external endpoint, which is consistent with the MITRE ATLAS AML.T0053 technique.
Detects reconnaissance activities where users submit prompts containing keywords typically used to probe LLM internals, such as system prompts, special instructions, or configuration details. This behavior is often indicative of precursor activity for prompt injection or jailbreak attempts.
Detects attempts to extract or override the system instructions of an Large Language Model (LLM) via prompt injection techniques, such as explicitly asking for the system prompt or commanding the model to ignore previous instructions.
This rule detects high volumes of near-identical, incrementally-varied queries directed at an AI model API. This behavior is indicative of a black-box adversarial optimization loop, where an attacker attempts to infer model vulnerabilities, bypass safety filters, or perform model evasion by observing how slight input variations change model responses.
This rule detects high volumes of near-identical, incrementally-varied queries directed at an AI model API. This behavior is indicative of a black-box adversarial optimization loop, where an attacker attempts to infer model vulnerabilities, bypass safety filters, or perform model evasion by observing how slight input variations change model responses.
This rule detects high volumes of near-identical, incrementally-varied queries directed at an AI model API. This behavior is indicative of a black-box adversarial optimization loop, where an attacker attempts to infer model vulnerabilities, bypass safety filters, or perform model evasion by observing how slight input variations change model responses.
Detects LLM jailbreak attempts using a 'crescendo' multi-turn strategy. The rule monitors for sequences where an initial prompt contains known jailbreak-intent keywords leading to an AI refusal, followed shortly by a compliant AI response to a rephrased request within the same conversation session.
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