Detects the first time a Vertex AI Agent Engine caller executes code in a sandboxEnvironment from a given source autonomous system. First-time sandbox execution by a user and source ASN can indicate a new workload, stolen credentials used from an unusual network, or early Agent Engine abuse before broader sandbox reuse is visible.

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Detects Vertex AI authenticated caller activity for the same client.user.email from at least three countries and three source IPs within a four-hour interval. That pattern can indicate stolen user or service-account credentials reused from multiple regions, including VPN or cloud egress and return trips where first and last locations are close.

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Detects the same Vertex AI authenticated caller (client.user.email) generating aiplatform audit activity from geographically distant source IPs within a short window at an impossible travel speed. That pattern can indicate a stolen user or service-account credential used from multiple regions. Related: GCP Vertex AI Caller Activity From High Number of Countries covers slower multi-country reuse without a high-speed hop.

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Detects Vertex AI prompt-response logs where a candidate safety_ratings.probability is HIGH or MEDIUM. Those values are stronger signals than NEGLIGIBLE or LOW alone. Requires the request to include safetySettings so ratings are logged.

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Detects a single source IP making a high number of Vertex AI GenerateContent (or StreamGenerateContent) audit calls against the same model resource in the lookback window. That pattern fits model extraction, automated scraping, or a compromised caller burning prediction quota.

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Detects a high number of Vertex AI prompt-response log rows and high total token usage for one model in the lookback window. Totals use provider usage_metadata.total_token_count (includes thinking tokens when billed). High request and token volume can indicate model extraction, quota abuse, or a compromised caller. Tune thresholds for your baseline.

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Detects Vertex AI GenerateContent prompts that match common jailbreak / instruction-override heuristics (for example ignore previous instructions, DAN or developer mode, unrestricted-AI language, or explicit safety-filter bypass). Typical prompt_response_logs exports do not populate HARM_CATEGORY_JAILBREAK (unlike hate/harassment/dangerous ratings); Model Armor findings remain the preferred first-party jailbreak signal when that API is enabled.

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Detects a GCP Vertex AI GenerateContent exchange whose prompt or model reply contains a known credential pattern (AWS access keys, GitHub tokens, PEM private keys, and similar), or whose assistant reply warns about exposed keys and revocation. Secrets in prompt/response logs are visible to anyone with access to BigQuery prompt-response export and Elastic.

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Detects changes to Vertex AI publisher model configuration via SetPublisherModelConfig (for example enabling or retargeting prompt/response BigQuery logging). Unexpected config changes can disable security-relevant logging or redirect logs.

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Detects three or more Vertex AI GenerateContent replies in 60 minutes whose assistant text indicates a refusal (cannot fulfill/assist/help/answer, unable to assist, decline the request, against my guidelines, and similar), aggregated by project and model. Prompt-response logs lack a stable caller identity, so this is a project-wide refusal burst signal — pivot to auditlogs for principal/IP attribution.

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Detects Vertex AI GenerateContent responses whose candidate finish_reason is SAFETY. That value is emitted when Gemini safety filters stop generation. Unlike textual refusals (finish_reason STOP), SAFETY means the provider blocked or truncated the completion.

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Detects Vertex AI GenerateContent requests that set safety_settings.threshold to BLOCK_NONE. That threshold disables category blocking for the listed harm categories when clients explicitly send safetySettings. Attackers and careless apps use BLOCK_NONE to weaken built-in Gemini filters.

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