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Introduction:
As organizations race to deploy autonomous AI agents, incident response remains the last puzzle piece they reluctantly address—until logic fails and chaos ensues. Amazon Bedrock AgentCore, AWS’s managed platform for building and operating AI agents at enterprise scale, has become a prime attack surface where identity compromise, prompt injection, and tool abuse converge into unprecedented security challenges. With over two million SDK downloads by December 2025, the urgency for structured incident response has never been greater.
Learning Objectives:
- Understand the five critical incident response domains specific to Amazon Bedrock AgentCore environments
- Master practical detection and mitigation techniques for AI agent-specific threats including prompt injection and memory poisoning
- Implement actionable Linux, Windows, and AWS CLI commands to investigate and contain AgentCore security incidents
You Should Know:
- Identity Compromise: When the JWT Keys to the Kingdom Are Stolen
AgentCore Identity manages agent authentication through OAuth 2.0, supporting providers like Amazon Cognito, Okta, Auth0, and Microsoft Entra ID. When a Cognito JWT or token vault is compromised, attackers can impersonate legitimate agents and traverse the entire multi-agent system.
Step-by-Step Investigation & Containment:
Linux/macOS – Extract and Validate JWT Claims:
Decode JWT token to inspect claims echo "eyJhbGciOiJSUzI1NiIs..." | cut -d. -f2 | base64 -d 2>/dev/null | jq . Check token expiration and issuer curl -X POST https://cognito-idp.region.amazonaws.com/ \ -H "X-Amz-Target: AWSCognitoIdentityProviderService.GetUser" \ -H "Authorization: Bearer $COMPROMISED_TOKEN"
AWS CLI – Revoke Compromised Credentials:
Revoke all refresh tokens for a compromised user aws cognito-idp admin-user-global-sign-out \ --user-pool-id $POOL_ID \ --username $COMPROMISED_USER List active agent runtime sessions aws bedrock-agentcore list-agent-runtimes \ --region us-east-1 --query 'agentRuntimes[?status==<code>ACTIVE</code>]' Force-terminate suspicious runtime aws bedrock-agentcore delete-agent-runtime --agent-runtime-id $RUNTIME_ID
Windows PowerShell – Audit Cognito User Pools:
Get Cognito user pool clients
Get-CGUserPoolClientList -UserPoolId $PoolId
Check for unusual JWT validation patterns
Get-CloudTrailEvent -EventName "GetUser" -StartTime (Get-Date).AddHours(-24) |
Where-Object {$_.UserIdentity.Type -eq "AssumedRole"}
Critical Action: Immediately rotate OIDC discovery URLs and implement token binding to prevent token replay across sessions.
- Agent Integrity: Prompt Injection, Memory Poisoning, and Supply-Chain Attacks
Agent integrity attacks target the agent’s reasoning process. Prompt injection can insert follow-on instructions into the agent’s context window, redirecting it toward data collection and exfiltration. More insidiously, indirect prompt injection can silently write adversary-controlled instructions into persistent long-term memory, propagating across future sessions without maintaining active access.
Step-by-Step Detection & Mitigation:
Enable Bedrock Guardrails in Policy (June 2026 Update):
AgentCore Policy now supports Bedrock Guardrails, evaluating agent actions and tool calls at the gateway boundary in real-time, blocking prompt injection and sensitive information leakage outside the agent’s code.
Create a guardrail with prompt injection detection
aws bedrock create-guardrail \
--1ame "agentcore-prompt-filter" \
--prompt-filter-config '{
"filters": [
{"type": "PROMPT_INJECTION", "action": "BLOCK"}
]
}'
Attach guardrail to AgentCore policy
aws bedrock-agentcore update-policy \
--policy-id $POLICY_ID \
--guardrail-id $GUARDRAIL_ID
Audit Memory Poisoning – Check Cross-Session Persistence:
Query CloudWatch logs for anomalous memory operations
aws logs filter-log-events \
--log-group-1ame /aws/bedrock-agentcore/memory \
--filter-pattern '{ $.eventType = "MemoryWrite" && $.content ~ "systeminstruction" }'
Linux – Monitor Suspicious MCP Tool Responses:
Monitor for suspicious MCP server responses tail -f /var/log/bedrock-agentcore/mcp-access.log | \ grep -E "(follow-on|redirect|collection|upload)"
Critical Action: Implement Automated Reasoning Checks—AWS’s neurosymbolic AI capability that verifies agent reasoning before action execution. Upgrade to bedrock-agentcore Python SDK version 1.6.1 or later to patch CVE-2026-12530 (improper neutralization of argument delimiters in install_packages()).
- Authorization Bypass: Cedar Policy Flips and Rogue Gateways
AgentCore’s Cedar-based Policy layer (GA as of March 3, 2026) enforces tool-invocation controls at the Gateway boundary. However, it operates at the gateway boundary and does not intercept in-context prompt manipulation occurring within the agent’s reasoning process prior to tool invocation. Attackers can flip Cedar policies or deploy rogue gateways to bypass authorization entirely.
Step-by-Step Policy Hardening:
Validate Cedar Policies with Cedar Analyzer:
Install Cedar CLI
curl -fsSL https://github.com/cedar-policy/cedar/releases/download/v3.0.0/cedar-x86_64-unknown-linux-musl.tar.gz | tar -xz
Validate policy against schema
./cedar validate \
--policies policy.cedar \
--schema schema.json \
--authorization
Example Cedar permit policy
cat > policy.cedar << 'EOF'
permit (
principal in Role::"Agent",
action in Action::"InvokeTool",
resource in Tool::"CodeInterpreter"
) when {
principal.toolLimit < 10 &&
resource.sensitivity != "CRITICAL"
};
EOF
AWS CLI – Detect Rogue Gateways:
List all gateways and check for unauthorized configurations
aws bedrock-agentcore list-gateways --query 'gateways[?status==<code>ACTIVE</code>]'
Review Cedar policy evaluation logs
aws logs filter-log-events \
--log-group-1ame /aws/bedrock-agentcore/policy \
--filter-pattern '{ $.decision = "DENY" }'
Linux – Monitor Policy Modification Attempts:
Monitor CloudTrail for unauthorized policy changes aws cloudtrail lookup-events \ --lookup-attributes AttributeKey=EventName,AttributeValue=UpdatePolicy \ --start-time $(date -d '24 hours ago' +%s) \ --query 'Events[?contains(CloudTrailEvent, <code>"errorCode":"AccessDenied"</code>)]'
Critical Action: Enforce IAM condition keys `bedrock-agentcore:subnets` and `bedrock-agentcore:securityGroups` to deny CreateAgentRuntime, CreateBrowser, and `CreateCodeInterpreter` unless referencing approved landing-zone subnet and security group IDs.
- Tool Abuse: Code Interpreter and Browser Sandbox Exploitation
The Code Interpreter component contains a documented privilege escalation path: any IAM principal holding `bedrock-agentcore:InvokeCodeInterpreter` permission can execute code under an agent’s IAM role, not the caller’s own role. The microVM exposes temporary execution-role credentials via an internal metadata service (MMDS), and researchers demonstrated string-filter bypass enabling credential exfiltration.
Step-by-Step Tool Abuse Mitigation:
Migrate from Sandbox Mode to VPC Mode:
Sandbox Mode does not provide complete network isolation—DNS resolution remains active, enabling covert C2 channels and data exfiltration.
Inventory all Code Interpreter instances aws bedrock-agentcore list-code-interpreters \ --query 'codeInterpreters[?networkMode==<code>SANDBOX</code>]' Migrate critical workloads to VPC mode aws bedrock-agentcore update-code-interpreter \ --interpreter-id $INTERPRETER_ID \ --1etwork-mode VPC \ --subnet-ids subnet-xxx subnet-yyy \ --security-group-ids sg-xxx
Apply Least-Privilege IAM Roles:
The default AgentCore Starter Toolkit role can include wide permissions: full DynamoDB access, full Secrets Manager access, and read access to all S3 buckets.
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Deny",
"Action": [
"s3:GetObject",
"secretsmanager:GetSecretValue"
],
"Resource": "",
"Condition": {
"StringNotEquals": {
"aws:ResourceTag/AgentCoreAccess": "approved"
}
}
}
]
}
Linux – Detect Code Interpreter Abuse:
Monitor for suspicious install_packages() calls (CVE-2026-12530) grep -r "install_packages" /var/log/bedrock-agentcore/ | \ grep -E "(--index-url| -r )" Check for DNS exfiltration attempts (Sandbox Mode) tcpdump -i any -1 port 53 | grep -v ".amazonaws.com"
Critical Action: Upgrade to bedrock-agentcore Python SDK version 1.6.1 or later and avoid passing user-supplied strings directly to install_packages().
- Observability Tampering: CloudTrail, Log Group, and KMS Manipulation
Attackers targeting observability tamper with CloudTrail, log groups, or KMS to blind defenders. Actions taken with compromised credentials are logged under the Code Interpreter’s identity, not the attacker’s—creating a forensic attribution gap. Control-plane mutations to Bedrock knowledge bases and RAG data sources via CloudTrail are detection opportunities.
Step-by-Step Observability Hardening:
Enable Comprehensive Telemetry:
Model Invocation Logging is not enabled by default—you must explicitly enable it.
Enable Model Invocation Logging
aws bedrock put-model-invocation-logging-configuration \
--logging-config '{
"cloudWatchConfig": {
"logGroupName": "/aws/bedrock/model-invocations",
"roleArn": "arn:aws:iam::account:role/BedrockLoggingRole"
},
"s3Config": {
"bucketName": "bedrock-logs-bucket",
"keyPrefix": "invocations/"
}
}'
Enable Agent Telemetry via ADOT SDK
Add to agent configuration:
-Dotel.traces.exporter=otlp \
-Dotel.exporter.otlp.endpoint=https://otel-collector:4317
Detect Observability Tampering:
Monitor CloudTrail for logging configuration changes aws cloudtrail lookup-events \ --lookup-attributes AttributeKey=EventName,AttributeValue=PutModelInvocationLoggingConfiguration \ --start-time $(date -d '7 days ago' +%s) Check for disabled CloudTrail trails aws cloudtrail describe-trails --query 'trailList[?IsMultiRegionTrail==<code>true</code> && LoggingEnabled==<code>false</code>]' Detect KMS key policy changes aws kms list-key-policies --key-id $KMS_KEY_ID
Windows PowerShell – Audit Log Group Integrity:
Check for unexpected log group deletions
Get-CloudTrailEvent -EventName "DeleteLogGroup" -StartTime (Get-Date).AddHours(-72) |
Format-Table -Property EventTime, UserIdentity, Resources
Verify retention policies
Get-CWLLogGroup | Where-Object {$_.RetentionInDays -lt 30}
Critical Action: Enable both observability pipelines (Model Invocation Logging and Agent Telemetry via ADOT SDK) simultaneously—they enable correlation queries essential for forensic investigations. Implement detection rules for `PrepareAgent` calls, which can make tampered configurations live.
What Undercode Say:
- Key Takeaway 1: Incident response for AI agents requires rethinking traditional security boundaries—the execution boundary problem (unbounded reasoning meeting bounded action) creates attack surfaces that don’t exist in conventional applications.
-
Key Takeaway 2: AWS’s design philosophy often treats observed behaviors as “intended functionality” rather than vulnerabilities (Sandbox DNS exfiltration, Code Interpreter privilege escalation). Organizations must implement their own compensating controls rather than relying solely on vendor fixes.
Analysis:
The proposed IR playbooks represent a watershed moment for AI agent security. Identity compromise (Cognito JWT theft) mirrors traditional IAM incidents but with amplified impact—compromised agent identities can traverse multi-agent systems autonomously. Agent integrity attacks (prompt injection, memory poisoning) introduce persistence mechanisms absent in conventional malware; poisoned memory persists across sessions without attacker presence. Authorization bypass through Cedar policy manipulation is uniquely dangerous because policies are authored in natural language or policy-as-code, introducing human error vectors. Tool abuse via Code Interpreter exploits the fundamental tension between agent autonomy and security control—the very capability that makes agents useful (executing code) is their greatest risk vector. Observability tampering is the attacker’s final act: blind the defender before executing the kill chain. The forensic attribution gap (actions logged under the interpreter’s identity, not the attacker’s) means traditional incident response timelines may be extended by days or weeks.
Prediction:
- +1 Regulatory Scrutiny Intensifies: The EU AI Act and similar frameworks will mandate mandatory incident response playbooks for AI agents, with AgentCore-specific requirements becoming industry benchmarks within 12-18 months.
-
+1 VPC Mode Becomes Default: Following the Sandbox Mode DNS exfiltration revelations, AWS will eventually deprecate Sandbox Mode or require explicit risk acknowledgment, similar to S3 public bucket changes.
-
-1 Supply-Chain Attacks Escalate: MCP server poisoning (analogous to malicious npm packages) will become the dominant attack vector, targeting AI tool invocation rather than software build pipelines.
-
-1 Memory Poisoning Exploits Emerge: As AgentCore Memory adoption grows, attackers will weaponize indirect prompt injection targeting long-term memory, creating persistent backdoors that evade traditional detection.
-
+1 Automated Reasoning Checks Become Mandatory: AWS’s neurosymbolic AI capabilities will evolve into mandatory guardrails for production AgentCore deployments, shifting from optional to required by 2027.
▶️ Related Video (78% Match):
https://www.youtube.com/watch?v=4YrduS-3Wjo
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IT/Security Reporter URL:
Reported By: Aondona Incidentresponse – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
