Hashing algorithms are an essential component of application security used for integrity checks, verifying authenticity, and protecting sensitive data. However, the full benefits of hashing are only realized when proper practices are followed. Suboptimal use of hashing undermines its security protections.
Here are key best practices for implementing and using hashing algorithms:
Use industry-standard algorithms.
Plenty of documented hash algorithms exist that have been vetted extensively by cryptography experts and have stood the test of time over many decades. Attempting to invent proprietary hashing schemes is highly inadvisable, as real-world vulnerabilities are often discovered through extensive cryptanalysis and attacks by researchers. These algorithms have been studied closely for weaknesses and optimized for efficient and secure implementations. Choose consensus picks recommended by standards bodies like NIST so you can leverage existing knowledge, tools, and libraries that have been thoroughly tested and validated across countless applications. Attempting to create new, untested algorithms is an unnecessary risk when strong standards exist.
Implement hashing correctly.
Hashing algorithms provide critical protections when implemented properly, but subtle implementation flaws can completely render hashing ineffective and vulnerable. Carefully follow specification details for elements like block size, padding, salting, and iteration counts as laid out in the algorithm standards and whitepapers. Even small deviations could introduce weaknesses that compromise the algorithm’s security guarantees. Thoroughly test your hashing implementations using known input values to verify that the correct outputs match the expected hashes. Compare against test vectors provided by standard bodies. Incorrect implementation often leaves openings, like reduced bit strength, that compromise security by deviating from the proven algorithm. Take care to implement all details precisely according to specification without taking shortcuts. Algorithms provide protection only when implemented correctly and without variation.
Use random salts.
Salts are random bits appended to inputs before hashing to prevent brute-force attacks using rainbow tables or other precomputed databases. But poor salt generation leads to collisions that completely cancel out the salting benefits. Use a cryptographically secure pseudo-random number generator with sufficient entropy to create salts that are truly unpredictable. Unique salts should be generated independently for each input being hashed to avoid any collisions. Store salts alongside hashed values to validate lookups. Never reuse the same salt twice. Match the original specific salt value when verifying instead of generating new salts to prevent substitution attacks. Salts introduce the necessary randomness to thwart statistical attacks against the hash. But salt reuse harms randomness, so new salts must be chosen per input instance.
Variations in input formatting like case, spacing, encoding, punctuation, and other syntactic details can lead to different hash outputs for identical semantic content. Normalize inputs before hashing to avoid these byproducts of inconsequential syntactic differences that don’t change meaning or message content. Techniques like case-folding, stripping whitespace, converting encodings, and removing special characters shift inputs to a consistent normalized representation for stable hashing based on meaning alone. Compare normalized hashes rather than raw unnormalized hashes to allow for inconsequential formatting differences that shouldn’t impact verification. Avoid denial of service by normalizing elements like trailing dot removal for DNS lookups. Normalize wisely to preserve meaning.
Pick the appropriate output size.
Larger hash output sizes exponentially increase the difficulty of producing collisions by expanding possibilities. But unnecessarily long hashes waste resources and slow down performance. 128-bit hashes offer modest protection for low-value, non-critical uses like data structure integrity checks where collisions are inconvenient but not catastrophic. 256-bit hashes significantly raise the bar for high-value assets like user credentials and API secrets that need much stronger protection against intentional collisions. Adjust the hash length proportional to the threat model and the risk of targeted attacks against the hashed data. Don’t reflexively maximize hash size if short hashes sufficiently deter attacks. Consider the performance impact of larger hashes on the application.
Use unique keys.
When hashing for message authentication, each sender should hash with a unique private key, or nonce. Signing different messages with the same shared key allows impersonation by any actor who obtains the key and could forge messages pretending to be the original sender. Issuing unique keys per device, user session, or message prevents spoofing by others sharing the same key. Stolen shared keys catastrophically weaken authentication for the whole system. But compromised unique keys only affect a specific user session or transaction localized to that nonce. Limit impact with per-message uniqueness.
Limit Access to Hashes
While hash outputs alone don’t reveal original inputs, access to hashes can substantially aid brute forcing of inputs through dictionary attacks. Follow strict need-to-know and least privilege practices when handling hashed values. Never display full hashes to end users or transmit hashes where they may leak outside the system. Store hashes very securely, ideally using a secretly protected database key unknown to users or most staff. Limiting hash visibility and access greatly thwarts reverse lookup attacks attempting to decrypt hashes through brute computational methods. Share hashes only with essential verification services.
Validate hash comparisons.
Naive direct hash comparisons may open timing-side channels for attackers attempting to guess inputs through repeated requests. Use constant-time string comparison functions important for hash validation. Ensure hash matching logic runs at a fixed time, unaffected by internal match results. Add randomized delays impervious to hash length to avoid variable timing that could leak clues through duration. Safe comparisons prevent external timing analysis attacks that attempt to extract hints about hashed values through subtle response variations.
Log Hashing Failures
Any failed hash validations may indicate an active malicious attack on the system. Carefully tracking and alerting on all verification failures spots potential threats extremely early before major damage can occur. Audit logs should include detailed timestamped forensic evidence around failed hash comparisons, including source, inputs, hashes, operations, stack traces, and metadata. Thoroughly scan logs to identify anomalous patterns of verification errors indicative of compromise, like brute force guessing attacks. Proactive monitoring can detect attacks that would otherwise go unnoticed.
When used properly, hashing provides tamper proofing, integrity verification, and authentication for applications. Consistently following these best practices for generation, storage, comparison, and overall hashing hygiene ensures hashes live up to their security guarantees. Hashing algorithms are powerful when wielded with care by skilled practitioners. Solutions like Appsealing allow easily integrating robust hashing capabilities into applications to effectively utilize their security benefits.