QuestionHow can a server be tricked into making harmful requests?
Understanding SSRF: Server-Side Request Forgery Explained
- medium level
- 60-sec video
- 5-min read
- 5-question quiz
The big idea
SSRF is a vulnerability where an attacker tricks a server into making unintended requests.
Your path
Log in to save progress
Explain it like I’m 10
Imagine your server is a delivery person. If someone gives them a wrong address, they might deliver something sensitive to the wrong place. SSRF is like that wrong address trick.
What is SSRF?
Server-Side Request Forgery (SSRF) is a security vulnerability that allows an attacker to send crafted requests from a vulnerable server to internal or external resources. This happens because the server, rather than the user's browser, is the entity making the request.
How SSRF Works
- User-Controlled Input: An attacker can manipulate a URL input that the server uses to make requests.
- Unintended Requests: This can lead the server to access resources it should not, such as internal APIs or cloud metadata endpoints.
- Internal Services Targeted: Internal services are often less protected, making them prime targets for SSRF attacks.
Why It Matters
- Cloud Metadata Risks: In cloud environments, SSRF can expose sensitive information like API keys or credentials.
- Destination Validation: Simply checking the hostname is not enough; DNS resolution and IP addresses must also be validated to prevent attacks.
- Redirects: If a request is redirected, the new destination must be validated again to avoid exploitation.
Defense Strategies
- Destination Validation: Ensure that the URL is safe before making requests.
- Allowlisting: Limit the destinations to which the server can connect.
- Egress Controls: Restrict where servers can send requests to minimize risk.
- Least Privilege: Limit the permissions of services to reduce potential damage from a compromise.
Key Takeaway
To protect against SSRF, control where your server can connect by implementing robust validation and access restrictions.
Where you’ll see this
SSRF vulnerabilities have been exploited in real-world attacks to access sensitive internal services and data.
How exams test this
Exams may test your understanding of SSRF concepts and defenses; be careful not to confuse SSRF with other request forgery types.
📖 Words to know
- SSRF
- Server-Side Request Forgery, a type of security vulnerability.
- Allowlisting
- A security measure that permits only specified resources.
Got it? Lock it in 🔒
5 quick questions. Students who test themselves remember far more than those who just re-read.
30-second revision card
- 1SSRF allows attackers to control server requests.
- 2Internal services and cloud metadata are common targets.
- 3Destination validation must include DNS and IP checks.
- 4Use allowlisting and egress controls to mitigate risks.
- 5Implement least privilege to limit service access.
Memory trick
SSRF = Server Sends Requests Foolishly.
🧠 Recall check
Answer in your head, then tap to flip. Recalling beats re-reading.
Myth vs Fact
❌ Myth: Only user browsers can be exploited.
✅ Fact: Servers can also be tricked into making harmful requests.
❌ Myth: Checking the URL is enough to prevent SSRF.
✅ Fact: DNS and IP validation are also necessary.
Ready to prove it? 🎯
You’ve revised the essentials. See how much actually stuck.
Question 1 of 5
Log in to take the quiz and lock this concept in
- 🎯Instant answers & explanationsSee why each answer is right, and the trap in the wrong ones.
- 📍Continue where you left offYour progress follows you on every device.
- 🧠Smart revision remindersTopics come back after 1 → 3 → 7 → 21 → 60 days, right before you’d forget.
- 🔥Streaks & activity heatmapBuild a daily habit, one short at a time.
- 📊Your weak spots, found for youSee which concepts need work and practise just those.
- 🔖Save topics for laterBookmark shorts to revisit before exams.
No credit card. Watching and reading stay free without an account.
Learn next
Watch first
Course outline · 301 topics
- 1What FAANG Interviewers Evaluate in System Design Interviews
- 2Understanding High-Level Design (HLD) and Low-Level Design (LLD)
- 3Understanding Functional and Non-Functional Requirements
- 4Understanding Scalable System Design in FAANG Companies
- 5Understanding Latency, Throughput, and Response Time
- 6Understanding Availability vs Reliability in System Design
- 7Understanding Latency and Throughput in System Design
- 8Understanding Vertical and Horizontal Scaling in System Design
- 9Stateless vs Stateful Servers: Scaling Made Easy
- 10Monolith vs Microservices: Choosing the Right Architecture
- 11Understanding Load Balancers in System Design
- 12L4 vs L7 Load Balancers: Key Differences Explained
- 13Understanding Reverse Proxy in High-Level Design
- 14Understanding CDN: How Cache Hits Improve Performance
- 15Understanding Cache Hits: Reducing Database Load
- 16Understanding the Cache-Aside Pattern in System Design
- 17Redis: Why Is It So Fast for High-Scale Systems?
- 18SQL vs NoSQL: Choosing the Right Database for Your Needs
- 19Understanding Database Replication in High-Level Design
- 20Understanding Sharding: How Databases Manage Large Data
- 21FAANG HLD 🔥 | Partition Pruning Explained — Scan Less, Query Faster! ⚡
- 22B-Tree Index: Efficient Database Navigation
- 23Read-After-Write Consistency in Database Systems
- 24Understanding Read Replicas in Database Scaling
- 25Identifying and Optimizing Database Bottlenecks
- 26Understanding CAP Theorem: Trade-offs in Distributed Systems
- 27Understanding Consistency Models in Distributed Systems
- 28Understanding Quorum Reads in Distributed Systems
- 29Leader Election in Distributed Systems: Handling Leader Failures
- 30Log Replication and Majority Commit in Distributed Systems
- 31Consistent Hashing: Key to Distributed Systems Scalability
- 32Distributed Caching: Why Spread It Out?
- 33Cache Eviction Policies: LRU, LFU, and FIFO Explained
- 34Cache Invalidation: TTL vs Freshness Explained
- 35Understanding Cache Stampede in High-Level Design
- 36Understanding Cache Penetration in System Design
- 37Understanding Cache Breakdown and Request Coalescing
- 38Token Bucket Rate Limiting Explained for Interviews
- 39Token Bucket vs Leaky Bucket: Key Differences Explained
- 40API Gateway: The Front Door of Your System
- 41API Gateway vs Proxy vs Load Balancer: Key Differences
- 42Service Discovery in Microservices Architecture
- 43DNS Service Discovery: Is DNS Enough for Microservices?
- 44Understanding DNS Record Types: A, CNAME, MX, and More
- 45Understanding DNS Resolution: How URLs Load in Browsers
- 46Understanding TCP 3-Way Handshake: SYN, SYN-ACK, ACK
- 47Understanding the TLS Handshake in HTTPS Connections
- 48HTTP vs HTTPS: Understanding the Key Differences
- 49Understanding HTTP Methods and Status Codes
- 50REST API Design: Building Scalable APIs for Real-World Use
- 51API Pagination: Efficiently Handling Large Datasets
- 52Database Indexing: How to Make Queries Fast
- 53Composite Indexes: Speed Up Multi-Column Queries in Databases
- 54Read vs Write Scaling: How to Scale Databases
- 55Database Replication: Scale and Survive Failures
- 56Sync vs Async Replication: Choosing the Right Approach
- 57Failover and Split-Brain in High-Level Design
- 58Understanding Write-Ahead Log (WAL) in Databases
- 59Understanding Transactions and ACID Properties in Databases
- 60Understanding Database Isolation Levels in Software Engineering
- 61Optimistic vs Pessimistic Locking in Database Management
- 62Understanding 2-Phase Commit in Distributed Transactions
- 63Understanding the Saga Pattern in Distributed Transactions
- 64Transactional Outbox Pattern: Never Lose Events!
- 65Understanding Idempotency in System Design
- 66Understanding Message Queues in Distributed Systems
- 67Queue vs Pub/Sub: Key Differences in System Design
- 68Understanding Apache Kafka for Scalable Event Streaming
- 69Understanding Kafka Partitioning for Scalable Messaging
- 70Message Delivery Semantics: At-Most-Once vs At-Least-Once vs Exactly-Once
- 71Understanding Kafka Consumer Lag and Its Impact
- 72Kafka Retention vs Compaction: History or Latest State?
- 73Kafka Exactly-Once Processing: Avoiding Duplicates
- 74Kafka Schema Evolution: Changing Schemas Without Breaking Consumers
- 75Kafka Producer Reliability: ACKs, Retries & Idempotence
- 76Kafka Ordering and Keys: Ensuring Message Order
- 77Kafka Consumer Offsets: Auto Commit vs Manual Commit
- 78Understanding Kafka Retries and Dead Letter Queue (DLQ)
- 79FAANG HLD 🔥 | Kafka Backpressure Explained — What Happens When Consumers Can't Keep Up? 🚨
- 80Understanding Kafka Brokers, Controllers, and KRaft Architecture
- 81Kafka Replication and ISR: Ensuring Data Availability
- 82Kafka Partition Reassignment: Move Replicas Without Data Loss
- 83Kafka Consumer Group Rebalancing Explained
- 84Kafka Consumer Assignment Strategies: Range vs RoundRobin vs Sticky
- 85Kafka Consumer Liveness: Heartbeats, Sessions & Timeouts Explained
- 86Kafka Consumer Lag Monitoring: Prevent Production Issues
- 87Kafka Lag Recovery Time: How Fast Can Consumers Catch Up?
- 88Kafka Consumer Fetch & Batching: Boosting Throughput
- 89Kafka Fetch Limits vs Poll Records: Understanding the Difference
- 90Kafka Eager vs Cooperative Rebalancing: Key Differences
- 91Kafka Static Membership: Reducing Unnecessary Rebalancing
- 92Understanding Kafka Rebalance Listeners in High-Level Design
- 93Understanding Kafka Offset Reset: Earliest vs Latest
- 94Kafka Replay Safely: Reprocess Messages Without Losing Data
- 95Kafka Commit Strategies: Auto vs Manual Commit Explained
- 96Kafka commitSync vs commitAsync: Choosing the Right Method
- 97Kafka Async Commit Ordering: Can Older Offsets Overwrite Newer Ones?
- 98Kafka Consumer Concurrency: Understanding Partitions and Threads
- 99Kafka Consumer Multithreading: Processing Messages in Parallel
- 100Kafka Consumer Parallelism: Partitions, Keys, and Throughput
- 101Kafka Consumer Pause & Resume: Managing Backpressure Smartly
- 102Kafka Backpressure Strategies: Handling Traffic Spikes
- 103Kafka Graceful Shutdown: Stop Without Losing Work
- 104Kafka Error Handling and Retry Strategies in HLD
- 105Kafka Retry Topics and Delayed Retries Explained
- 106Understanding Kafka Dead Letter Topics and Poison Messages
- 107Kafka Poison Messages: Risks of Infinite Retries
- 108Kafka Consumer Reprocessing: Safe Event Replay Strategies
- 109Kafka Replay Without Breaking Production: Safe Architecture
- 110Kafka Pause vs Seek: Key Differences Explained
- 111FAANG HLD 🔥 | Kafka Seek — Replay Specific Messages Without Replaying Everything! 🎯
- 112Kafka subscribe() vs assign(): Who Controls Partition Assignment?
- 113Kafka Manual Partition Assignment: Using assign() for Control
- 114Kafka Rebalance: Avoiding Work Loss or Duplication
- 115Kafka Consumer Crash Recovery: Resuming Processing Explained
- 116Kafka Consumer State: Position, Offset, and Business State
- 117Kafka Stateful Stream Processing: How Does It Remember?
- 118Kafka Streams vs Consumer: Choosing the Right Tool
- 119Kafka Streams Local State: Fast Processing and Recovery
- 120Kafka Streams Windowing: Tumbling vs Hopping Windows Explained
- 121Distributed Locks: Preventing Duplicate Work Across Servers
- 122Choosing a Distributed Lock: Redis vs Database vs ZooKeeper
- 123Leader Election in Distributed Systems Explained
- 124Leader Election Algorithms: Bully vs Raft vs ZooKeeper
- 125Understanding Consensus in Distributed Systems
- 126Understanding Raft Consensus: Terms, Elections & Log Replication
- 127Understanding Raft Log Replication: matchIndex vs commitIndex
- 128Raft Leader Failure and Re-election Process Explained
- 129FAANG HLD 🔥 | Raft Safety — Why Committed Entries Survive Leader Failure! 🛡️
- 130Understanding Quorum and Majority in Distributed Systems
- 131Paxos Consensus: Understanding Distributed Agreement
- 132FAANG HLD 🔥 | Raft vs Paxos — What's the Difference? Consensus Explained! ⚡
- 133ZooKeeper Architecture and ZAB Explained
- 134Understanding ZooKeeper Ephemeral and Sequential Nodes
- 135ZooKeeper Watches: Avoiding the Thundering Herd Problem
- 136HLD: Crash Failures vs Network Failures in Distributed Systems
- 137HLD: Fail-Stop vs Fail-Recover Explained
- 138Understanding Network Partitions in Distributed Systems
- 139Understanding Partial Failure in Distributed Systems
- 140HLD: Understanding Server Failure Detection and Timeouts
- 141Understanding Distributed Clocks in Systems Design
- 142Understanding Physical vs Logical Time in Distributed Systems
- 143Understanding Lamport Timestamps in Distributed Systems
- 144Understanding Vector Clocks in Distributed Systems
- 145Understanding Causality in Distributed Systems
- 146Understanding RPC: Remote Procedure Calls in Distributed Systems
- 147HLD: Understanding REST, RPC, and gRPC Differences
- 148Understanding gRPC Architecture: Behind the Call
- 149Protobuf and Schema Evolution: Avoid Breaking Changes
- 150Understanding Unary vs Streaming RPC in gRPC
- 151HLD: Synchronous vs Asynchronous Communication Explained
- 152HLD: Understanding Request-Response vs Events in System Design
- 153HLD: 5 Microservices Communication Patterns Explained
- 154Connection Pooling Explained: Why a Bigger Pool Can Hurt
- 155Keep-Alive in Networking: Efficient HTTP Connections
- 156When to Use Microservices in Software Design
- 157HLD: Microservices vs Modular Monolith Explained
- 158Choosing Service Boundaries in Microservices Architecture
- 159Understanding Database per Service in Microservices Architecture
- 160HLD: Shared DB vs Database per Service Trade-Offs
- 161HLD: Circuit Breaker Explained to Prevent Failures
- 162Understanding the Retry Pattern in System Design
- 163HLD Timeout Pattern: Understanding Timeouts in Distributed Systems
- 164Understanding the Bulkhead Pattern in System Design
- 165HLD: Fallbacks Explained for System Design
- 166Understanding Retry Storms in Distributed Systems
- 167Exponential Backoff: Managing Retry Storms in Systems
- 168Understanding Jitter in High-Level Design
- 169Understanding Circuit Breaker: CLOSED, OPEN, and HALF-OPEN States
- 170Cascading Failures in Distributed Systems Explained
- 171Load Shedding in Distributed Systems: Protecting Capacity
- 172Graceful Degradation in System Design
- 173Distributed Backpressure: Managing Service Overload
- 174HLD: Fail Fast vs Fail Safe in System Design
- 175HLD: Managing Dependency Failures in Distributed Systems
- 176Understanding Logs, Metrics, and Traces in Software Engineering
- 177Observability in Distributed Systems: Debugging Production Issues
- 178Structured Logging: Debugging Production Issues Faster
- 179Understanding Correlation IDs in Microservices Architecture
- 180Understanding Distributed Tracing in Microservices Architecture
- 181Understanding the RED Method for Microservices Monitoring
- 182Understanding the USE Method for Infrastructure Bottlenecks
- 183Understanding SLIs, SLOs, and SLAs in Reliability Engineering
- 184Understanding Error Budgets in High-Level Design
- 185Understanding Alerting in High-Level Design
- 186HLD: Search Engine Architecture and Its Components
- 187Inverted Index: How Search Engines Find Pages Efficiently
- 188HLD: Tokenization and Analyzers in Search Engines
- 189HLD: Stemming and Normalization in Search Engines
- 190HLD: Full-Text Search vs Database Search with Elasticsearch
- 191Elasticsearch Architecture: How Distributed Search Works
- 192Elasticsearch Shards vs Replicas: Partitioning vs Duplication
- 193Elasticsearch Indexing: Why Can't You Search Your Document Yet?
- 194Elasticsearch Query Execution: How Searches Work Across Shards
- 195Elasticsearch Refresh: Why Your Document Isn’t Searchable Yet
- 196Understanding Elasticsearch Relevance Scoring with BM25
- 197Elasticsearch Filters vs Queries: MUST vs FILTER Explained
- 198Elasticsearch Aggregations: Metrics vs Buckets Explained
- 199Elasticsearch Pagination: From & Size vs Search After
- 200Understanding Elasticsearch ILM: Hot, Warm, Cold Phases
- 201Elasticsearch Aliases and Rollover: Zero-Downtime Index Switching
- 202Understanding Elasticsearch Data Streams in HLD
- 203Understanding Elasticsearch Index Templates and Components
- 204Elasticsearch Mapping: Dynamic vs Explicit Explained
- 205Understanding Elasticsearch Shards and Replicas in HLD
- 206Elasticsearch Cluster Sizing: How Many Nodes Do You Need?
- 207Understanding Elasticsearch Node Roles for Scaling Clusters
- 208Elasticsearch Split Brain: Master Election and Quorum Explained
- 209Understanding Elasticsearch Snapshots: Replicas vs Backups
- 210Understanding Elasticsearch Cross-Cluster Replication (CCR)
- 211Understanding Elasticsearch ILM for Cost Management
- 212Elasticsearch Data Streams vs. Time-Based Indices: Which to Choose?
- 213Elasticsearch Zero-Downtime Reindexing: The Alias Switch Trick
- 214Elasticsearch Reindexing: Understanding the Dual-Write Trap
- 215Elasticsearch Query Optimization: Filters and Caching
- 216Elasticsearch Pagination: Fixing Slow Page Searches
- 217Elasticsearch Aggregations: Buckets, Metrics, and Cardinality
- 218Elasticsearch Autocomplete: Completion vs. Edge N-Grams vs. Search-as-You-Type
- 219Elasticsearch Fuzzy Search: Handling Typos in Queries
- 220Understanding Elasticsearch Synonyms and Analyzers
- 221Elasticsearch BM25 & Field Boosting: Ranking Search Results
- 222Elasticsearch Function Score: Ranking with Business Signals
- 223Elasticsearch Rescoring: Improve Search Result Rankings
- 224Understanding Elasticsearch Hybrid Search: BM25 vs Vector Search
- 225Understanding Elasticsearch Semantic Search and Embeddings
- 226Dynamo-Style Databases: High-Level Design Overview
- 227Consistent Hashing: Efficient Node Management in Distributed Systems
- 228Quorum-Based Replication: How Many Replicas Must Agree?
- 229Hinted Handoff: Handling Replica Failures in Databases
- 230Cassandra Architecture: Masterless Database Scaling Explained
- 231Understanding Cassandra Partition Keys for Data Storage
- 232Cassandra Clustering Keys: How Rows Are Sorted
- 233MongoDB Architecture: Understanding Replica Sets and Sharding
- 234MongoDB Sharding: Scaling to Millions of Orders
- 235Time-Series Database Architecture: Storing Millions of Metrics
- 236Time-Series Data Modeling: Avoiding Cardinality Issues
- 237Understanding Retention Policies in Databases
- 238Time-Based Partitioning: Scaling Time-Series Databases
- 239HLD: Downsampling and Aggregation in Time-Series Databases
- 240Understanding OLTP and OLAP in Database Design
- 241Data Warehouse Architecture: Analyzing Business Data Efficiently
- 242Data Lake Architecture: Storing Big Data at Scale
- 243Data Lakehouse Architecture: Combining Lakes and Warehouses
- 244HLD: Batch vs Stream Processing in System Design
- 245Stream Processing Architecture: From Events to Real-Time Insights
- 246Understanding Event Time vs Processing Time in Stream Processing
- 247Understanding Watermarks in Stream Processing
- 248HLD: Late-Arriving Events - Drop, Update, or Replay?
- 249Tumbling vs Hopping Windows in Stream Processing
- 250ETL vs ELT: Where Should Data Transformation Happen?
- 251Change Data Capture (CDC): Real-Time Database Syncing
- 252HLD: Database CDC with Kafka for Real-Time Events
- 253Event Sourcing: Store What Happened, Rebuild the State
- 254CQRS Explained: Commands vs Queries in System Design
- 255HLD: Authentication vs Authorization Explained
- 256Session-Based Authentication: How Cookies & Sessions Work
- 257Understanding JWT Authentication: How JSON Web Tokens Work
- 258OAuth 2.0: Access Data Without Sharing Your Password
- 259Understanding OpenID Connect (OIDC) for Authentication
- 260Understanding API Keys: Identification and Security
- 261Access Tokens vs Refresh Tokens: How Token Renewal Works
- 262Understanding Token Expiration and Refresh Token Rotation
- 263Understanding mTLS: Microservices Authentication Explained
- 264Zero Trust Architecture: Never Trust, Always Verify
- 265Encryption at Rest vs In Transit: Protecting Your Data
- 266Symmetric vs Asymmetric Encryption: Key Differences Explained
- 267Hashing vs Encryption: Key Differences Explained
- 268HLD: Password Hashing and Salt Explained
- 269HLD — KMS Explained 🔐 What If Your Encryption Key Gets Stolen?
- 270HLD — SQL Injection 💀 How One Input Can Attack Your Database!
- 271Understanding Cross-Site Scripting (XSS) in Web Security
- 272Understanding CSRF: Protecting Against Unwanted Requests
- 273Understanding SSRF: Server-Side Request Forgery Explained
- 274Secure API Design: 7 Essential Rules for Backend Engineers
- 275HLD: Single Region vs Multi-Region Architecture
- 276Active-Passive Architecture: Handling Production Downtime
- 277Understanding Active-Active Architecture in Distributed Systems
- 278Global Load Balancing: How the Internet Routes You to Regions
- 279GeoDNS: How DNS Routes Users to the Right Region
- 280Cross-Region Database Replication: Keeping Databases in Sync
- 281HLD: Multi-Region Data Consistency Explained
- 282Understanding Data Residency in High-Level Design
- 283Global Failover: What Happens When an Entire Region Goes Down?
- 284Disaster Recovery Across Regions: Can Your System Recover?
- 285Understanding RPO and RTO in Disaster Recovery
- 286Backup Strategies: Full vs Incremental vs Differential
- 287Point-in-Time Recovery: Recovering Deleted Database Data
- 288Disaster Recovery Testing: Ensuring Your DR Plan Works
- 289Understanding High Availability in System Design
- 290Understanding Fault Domains in High-Level Design
- 291Understanding Availability Zones in High-Level Design
- 292Understanding N+1 Redundancy in High-Level Design
- 293Understanding SPOF: Why Your App Can Go Down with Healthy Servers
- 294Understanding Containers vs Virtual Machines in HLD
- 295Container Networking: How Do Containers Communicate?
- 296HLD: Understanding Container Storage and Data Persistence
- 297Container Health Checks: Ensuring Your App Is Healthy
- 298Understanding Kubernetes Architecture: Control Plane vs Worker Nodes
- 299Understanding Kubernetes Pods and Deployments
- 300Kubernetes Services: Stable Endpoints for Dynamic Pods
- 301Kubernetes Ingress: One Entry Point for Multiple Apps
Connected concepts

⚡ One HLD concept. 60 seconds. Interview ready
Understanding CSRF: Protecting Against Unwanted Requests
CSRF tricks your browser into making requests without your consent.

⚡ One HLD concept. 60 seconds. Interview ready
Understanding Cross-Site Scripting (XSS) in Web Security
XSS is a vulnerability that lets attackers run scripts in users' browsers.

⚡ One HLD concept. 60 seconds. Interview ready
Understanding SPOF: Why Your App Can Go Down with Healthy Servers
A single component's failure can take down your entire application, even with redundancy.

⚡ One HLD concept. 60 seconds. Interview ready
Secure API Design: 7 Essential Rules for Backend Engineers
Secure API design requires multiple strategies to protect sensitive data and access.

⚡ One HLD concept. 60 seconds. Interview ready
HLD — SQL Injection 💀 How One Input Can Attack Your Database!
SQL injection is a security risk where attackers manipulate database queries via input fields.

⚡ One HLD concept. 60 seconds. Interview ready
Session-Based Authentication: How Cookies & Sessions Work
Session-based authentication uses cookies to remember users after login.