CWE-1339 Base Draft

Insufficient Precision or Accuracy of a Real Number

This vulnerability occurs when a program uses a data type or algorithm that cannot accurately represent or calculate the fractional part of a real number, leading to incorrect results in…

Definition

What is CWE-1339?

This vulnerability occurs when a program uses a data type or algorithm that cannot accurately represent or calculate the fractional part of a real number, leading to incorrect results in security-critical operations.
Many security decisions, like financial transactions or cryptographic calculations, depend on extreme numerical precision. When a system uses floating-point arithmetic or other imprecise representations for these tasks, tiny rounding errors can create exploitable gaps. Attackers can manipulate these inaccuracies to trigger incorrect authorization, cause financial loss, or corrupt data. Computers fundamentally struggle to store certain fractional values exactly. Issues arise from finite storage (like limited bits in a float), repeating decimals in binary (like 0.1), or irrational numbers (like pi). The chosen representation may truncate or round the number, causing the program's internal calculation to drift from the mathematically correct result. Developers must therefore select numeric types and libraries that guarantee the required precision for their specific domain.
Real-world impact

Real-world CVEs caused by CWE-1339

  • CVE-2018-16069

    Chain: series of floating-point precision errors (CWE-1339) in a web browser rendering engine causes out-of-bounds read (CWE-125), giving access to cross-origin data

  • CVE-2017-7619

    Chain: rounding error in floating-point calculations (CWE-1339) in image processor leads to infinite loop (CWE-835)

  • CVE-2021-29529

    Chain: machine-learning product can have a heap-based buffer overflow (CWE-122) when some integer-oriented bounds are calculated by using ceiling() and floor() on floating point values (CWE-1339)

  • CVE-2008-2108

    Chain: insufficient precision (CWE-1339) in random-number generator causes some zero bits to be reliably generated, reducing the amount of entropy (CWE-331)

  • CVE-2006-6499

    Chain: web browser crashes due to infinite loop - "bad looping logic [that relies on] floating point math [CWE-1339] to exit the loop [CWE-835]"

How attackers exploit it

Step-by-step attacker path

  1. 1

    Muller's Recurrence is a series that is supposed to converge to the number 5. When running this series with the following code, different implementations of real numbers fail at specific iterations:

  2. 2

    The chart below shows values for different data structures in the rust language when Muller's recurrence is executed to 80 iterations. The data structure f64 is a 64 bit float. The data structures IF are fixed representations 128 bits in length that use the first number as the size of the integer and the second size as the size of the fraction (e.g. I16F112 uses 16 bits for the integer and 112 bits for the fraction). The data structure of Ratio comes in three different implementations: i32 uses a ratio of 32 bit signed integers, i64 uses a ratio of 64 bit signed integers and BigInt uses a ratio of signed integer with up to 2^32 digits of base 256. Notice how even with 112 bits of fractions or ratios of 64bit unsigned integers, this math still does not converge to an expected value of 5.

  3. 3

    On February 25, 1991, during the eve of the Iraqi invasion of Saudi Arabia, a Scud missile fired from Iraqi positions hit a US Army barracks in Dhahran, Saudi Arabia. It miscalculated time and killed 28 people [REF-1190].

  4. 4

    Sleipner A, an offshore drilling platform in the North Sea, was incorrectly constructed with an underestimate of 50% of strength in a critical cluster of buoyancy cells needed for construction. This led to a leak in buoyancy cells during lowering, causing a seismic event of 3.0 on the Richter Scale and about $700M loss [REF-1281].

Vulnerable code example

Vulnerable Rust

Muller's Recurrence is a series that is supposed to converge to the number 5. When running this series with the following code, different implementations of real numbers fail at specific iterations:

Vulnerable Rust 
fn rec_float(y: f64, z: f64) -> f64 
 {

```
   108.0 - ((815.0 - 1500.0 / z) / y);
 }
 fn float_calc(turns: usize) -> f64 
 {
   let mut x: Vec<f64> = vec![4.0, 4.25];
   (2..turns + 1).for_each(|number| 
   {
  	 x.push(rec_float(x[number - 1], x[number - 2]));
   });
   x[turns]
 }
Secure code example

Secure Rust

The chart below shows values for different data structures in the rust language when Muller's recurrence is executed to 80 iterations. The data structure f64 is a 64 bit float. The data structures IF are fixed representations 128 bits in length that use the first number as the size of the integer and the second size as the size of the fraction (e.g. I16F112 uses 16 bits for the integer and 112 bits for the fraction). The data structure of Ratio comes in three different implementations: i32 uses a ratio of 32 bit signed integers, i64 uses a ratio of 64 bit signed integers and BigInt uses a ratio of signed integer with up to 2^32 digits of base 256. Notice how even with 112 bits of fractions or ratios of 64bit unsigned integers, this math still does not converge to an expected value of 5.

Secure Rust 
Use num_rational::BigRational;

 fn rec_big(y: BigRational, z: BigRational) -> BigRational
 {

```
   BigRational::from_integer(BigInt::from(108))
  	 - ((BigRational::from_integer(BigInt::from(815))
  	 - BigRational::from_integer(BigInt::from(1500)) / z)
  	 / y)
 }
 fn big_calc(turns: usize) -> BigRational 
 {
   let mut x: Vec<BigRational> = vec![BigRational::from_float(4.0).unwrap(), BigRational::from_float(4.25).unwrap(),];
   (2..turns + 1).for_each(|number| 
   {
  	 x.push(rec_big(x[number - 1].clone(), x[number - 2].clone()));
   });
   x[turns].clone()
 }
What changed: the unsafe sink is replaced (or the input is validated/escaped) so the same payload no longer triggers the weakness.
Prevention checklist

How to prevent CWE-1339

  • Implementation / Patching and Maintenance The developer or maintainer can move to a more accurate representation of real numbers. In extreme cases, the programmer can move to representations such as ratios of BigInts which can represent real numbers to extremely fine precision. The programmer can also use the concept of an Unum real. The memory and CPU tradeoffs of this change must be examined. Since floating point reals are used in many products and many locations, they are implemented in hardware and most format changes will cause the calculations to be moved into software resulting in slower products.
Detection signals

How to detect CWE-1339

SAST High

Run static analysis (SAST) on the codebase looking for the unsafe pattern in the data flow.

DAST Moderate

Run dynamic application security testing against the live endpoint.

Runtime Moderate

Watch runtime logs for unusual exception traces, malformed input, or authorization bypass attempts.

Code review Moderate

Code review: flag any new code that handles input from this surface without using the validated framework helpers.

Plexicus auto-fix

Plexicus auto-detects CWE-1339 and opens a fix PR in under 60 seconds.

Codex Remedium scans every commit, identifies this exact weakness, and ships a reviewer-ready pull request with the patch. No tickets. No hand-offs.

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Frequently asked questions

Frequently asked questions

What is CWE-1339?

This vulnerability occurs when a program uses a data type or algorithm that cannot accurately represent or calculate the fractional part of a real number, leading to incorrect results in security-critical operations.

How serious is CWE-1339?

MITRE has not published a likelihood-of-exploit rating for this weakness. Treat it as medium-impact until your threat model proves otherwise.

What languages or platforms are affected by CWE-1339?

MITRE lists the following affected platforms: Not OS-Specific, Not Architecture-Specific, Not Technology-Specific.

How can I prevent CWE-1339?

The developer or maintainer can move to a more accurate representation of real numbers. In extreme cases, the programmer can move to representations such as ratios of BigInts which can represent real numbers to extremely fine precision. The programmer can also use the concept of an Unum real. The memory and CPU tradeoffs of this change must be examined. Since floating point reals are used in many products and many locations, they are implemented in hardware and most format changes will cause…

How does Plexicus detect and fix CWE-1339?

Plexicus's SAST engine matches the data-flow signature for CWE-1339 on every commit. When a match is found, our Codex Remedium agent opens a fix PR with the corrected code, tests, and a one-line summary for the reviewer.

Where can I learn more about CWE-1339?

MITRE publishes the canonical definition at https://cwe.mitre.org/data/definitions/1339.html. You can also reference OWASP and NIST documentation for adjacent guidance.

Related weaknesses

Weaknesses related to CWE-1339

CWE-682 Parent

Incorrect Calculation

This vulnerability occurs when software performs a calculation that produces wrong or unexpected results, which are then used to make…

CWE-128 Sibling

Wrap-around Error

A wrap-around error happens when a variable exceeds the maximum value its data type can hold, causing it to unexpectedly reset to a very…

CWE-131 Sibling

Incorrect Calculation of Buffer Size

This vulnerability occurs when a program miscalculates the amount of memory needed for a buffer, potentially leading to a buffer overflow…

CWE-1335 Sibling

Incorrect Bitwise Shift of Integer

This vulnerability occurs when a program attempts to shift an integer's bits by an invalid amount—either a negative number or a value…

CWE-135 Sibling

Incorrect Calculation of Multi-Byte String Length

This vulnerability occurs when software incorrectly measures the length of strings containing multi-byte or wide characters, leading to…

CWE-190 Sibling

Integer Overflow or Wraparound

Integer overflow or wraparound occurs when a calculation produces a numeric result that exceeds the maximum value a variable can hold.…

CWE-191 Sibling

Integer Underflow (Wrap or Wraparound)

Integer underflow occurs when a subtraction operation results in a value smaller than the data type's minimum limit, causing the value to…

CWE-193 Sibling

Off-by-one Error

An off-by-one error occurs when a program incorrectly calculates a boundary, such as a loop counter or array index, by being one unit too…

CWE-369 Sibling

Divide By Zero

A divide-by-zero error occurs when software attempts to perform a division operation where the denominator is zero.

Resources

Further reading

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