AWS Bedrock AI Platform Exposed to Eight Critical Attack Vectors, Research Reveals
Amazon’s AWS Bedrock a platform enabling developers to build AI-powered applications by integrating foundation models with enterprise data and systems has been identified as a high-value target for attackers. Security researchers at XM Cyber uncovered eight validated attack vectors that exploit Bedrock’s connectivity to critical infrastructure, including Salesforce, Lambda functions, SharePoint, and vector databases.
The vulnerabilities stem from misconfigured permissions and weak access controls, allowing attackers to manipulate logs, compromise knowledge bases, hijack AI agents, inject malicious workflows, degrade security guardrails, and poison prompts. Each vector begins with minimal privileges but can escalate to full system compromise.
Key Attack Vectors
- Model Invocation Log Attacks – Attackers can redirect or delete logs stored in S3 buckets, harvesting sensitive data or erasing forensic evidence.
- Knowledge Base Attacks (Data Source) – By accessing S3, Salesforce, or SharePoint credentials, attackers bypass AI models to extract raw data or move laterally into Active Directory.
- Knowledge Base Attacks (Data Store) – Compromised credentials for vector databases (Pinecone, Redis) or AWS-native stores (Aurora, Redshift) grant full access to structured enterprise data.
- Agent Attacks (Direct) – Modifying agent prompts or attaching malicious executors enables unauthorized actions, such as database tampering or user creation.
- Agent Attacks (Indirect) – Injecting malicious code into Lambda functions allows data exfiltration or model response manipulation.
- Flow Attacks – Altering workflows to reroute data to attacker-controlled endpoints or bypassing authorization checks via modified condition nodes.
- Guardrail Attacks – Weakening or removing content filters increases susceptibility to prompt injection and toxic output generation.
- Managed Prompt Attacks – Modifying centralized prompt templates enables mass-scale data exfiltration or harmful content generation without detection.
Impact & Implications
The research highlights that attackers target Bedrock’s integrations rather than the AI models themselves. A single over-privileged identity can redirect logs, hijack agents, or access on-premises systems. Security teams must map attack paths across cloud and hybrid environments while enforcing strict permission controls to mitigate risks.
The findings underscore the need for comprehensive visibility into AI workloads and their associated permissions to prevent exploitation. Full technical details, including architectural diagrams, are available in XM Cyber’s research report.
Source: https://thehackernews.com/2026/03/we-found-eight-attack-vectors-inside.html
Amazon TPRM report: https://www.rankiteo.com/company/amazon-web-services
Pinecone TPRM report: https://www.rankiteo.com/company/pinecone-io
Salesforce TPRM report: https://www.rankiteo.com/company/salesforce
Microsoft TPRM report: https://www.rankiteo.com/company/microsoft-azure
Redis TPRM report: https://www.rankiteo.com/company/redis-labs-inc
Amazon Aurora TPRM report: https://www.rankiteo.com/company/amazon-web-services
Amazon Redshift TPRM report: https://www.rankiteo.com/company/amazon-web-services
"id": "salamamicpinred1774269319",
"linkid": "salesforce, amazon-web-services, microsoft-azure, pinecone-io, redis-labs-inc",
"type": "Vulnerability",
"date": "3/2026",
"severity": "100",
"impact": "5",
"explanation": "Attack threatening the organization's existence"{'affected_entities': [{'customers_affected': 'Enterprises using AWS Bedrock '
'for AI-powered applications',
'industry': 'Technology, Cloud Computing, AI',
'location': 'Global',
'name': 'Amazon Web Services (AWS)',
'size': 'Large Enterprise',
'type': 'Cloud Service Provider'}],
'attack_vector': ['Model Invocation Log Attacks',
'Knowledge Base Attacks (Data Source)',
'Knowledge Base Attacks (Data Store)',
'Agent Attacks (Direct)',
'Agent Attacks (Indirect)',
'Flow Attacks',
'Guardrail Attacks',
'Managed Prompt Attacks'],
'data_breach': {'data_exfiltration': 'Possible via malicious workflows, '
'Lambda functions, or '
'attacker-controlled endpoints',
'personally_identifiable_information': 'Likely (due to access '
'to logs, databases, '
'and enterprise '
'systems)',
'sensitivity_of_data': 'High (personally identifiable '
'information, enterprise data, AI '
'training data)',
'type_of_data_compromised': ['Logs (sensitive data)',
'Raw enterprise data',
'Structured data (vector '
'databases)',
'AI model responses',
'Credentials (S3, Salesforce, '
'SharePoint, etc.)']},
'description': 'Amazon’s AWS Bedrock, a platform enabling developers to build '
'AI-powered applications by integrating foundation models with '
'enterprise data and systems, has been identified as a '
'high-value target for attackers. Security researchers at XM '
'Cyber uncovered eight validated attack vectors that exploit '
'Bedrock’s connectivity to critical infrastructure, including '
'Salesforce, Lambda functions, SharePoint, and vector '
'databases. The vulnerabilities stem from misconfigured '
'permissions and weak access controls, allowing attackers to '
'manipulate logs, compromise knowledge bases, hijack AI '
'agents, inject malicious workflows, degrade security '
'guardrails, and poison prompts. Each vector begins with '
'minimal privileges but can escalate to full system '
'compromise.',
'impact': {'brand_reputation_impact': 'Potential reputational damage due to '
'AI security vulnerabilities and data '
'exposure',
'data_compromised': 'Sensitive data in logs, raw enterprise data, '
'structured data in vector databases, AI model '
'responses',
'identity_theft_risk': 'High (due to access to personally '
'identifiable information and sensitive '
'data)',
'operational_impact': 'Unauthorized actions (e.g., database '
'tampering, user creation), data '
'exfiltration, model response manipulation, '
'bypassing authorization checks',
'systems_affected': 'AWS Bedrock, S3 buckets, Salesforce, Lambda '
'functions, SharePoint, vector databases '
'(Pinecone, Redis), Aurora, Redshift, Active '
'Directory'},
'investigation_status': 'Research Findings Published',
'lessons_learned': 'Attackers target AI platform integrations rather than the '
'models themselves. Over-privileged identities can lead to '
'full system compromise. Comprehensive visibility into AI '
'workloads and permissions is critical for security.',
'post_incident_analysis': {'corrective_actions': 'Enforce least-privilege '
'access, map attack paths, '
'enhance monitoring, audit '
'security configurations',
'root_causes': 'Misconfigured permissions, weak '
'access controls, over-privileged '
'identities, lack of visibility '
'into AI workloads'},
'recommendations': ['Enforce strict permission controls and least-privilege '
'access for AI workloads',
'Map attack paths across cloud and hybrid environments to '
'identify risks',
'Enhance monitoring and visibility into AI workloads and '
'associated permissions',
'Regularly audit and update security configurations for '
'AI platforms and integrations'],
'references': [{'source': 'XM Cyber Research Report'}],
'response': {'enhanced_monitoring': 'Recommended to prevent exploitation',
'remediation_measures': 'Enforce strict permission controls, map '
'attack paths across cloud and hybrid '
'environments, enhance visibility into '
'AI workloads and associated '
'permissions'},
'stakeholder_advisories': 'Security teams advised to review AWS Bedrock '
'configurations and enforce strict permission '
'controls',
'title': 'AWS Bedrock AI Platform Exposed to Eight Critical Attack Vectors, '
'Research Reveals',
'type': 'Misconfiguration, Privilege Escalation, Data Exfiltration, AI '
'Security',
'vulnerability_exploited': 'Misconfigured permissions, weak access controls, '
'over-privileged identities'}